Make your application to DREAM


If you are interested in joining us in DREAM to study for a PhD in Big Data, Risk and Environmental Analytical methods, we look forward to hearing from you. Please note the University proposing each of the projects and be sure to contact the respective team regarding any further details you need. We have 15 funded positions open (5 before July and 10 from October, 2017) for the right applicants, to be drawn from across the following list of project proposals across the four partner universities.


MS Word document
DREAM CDT Student Application Form »

Note, when completed, please email the form (plus additional documents as directed) to the respective university contact shown in the listings below.


Cranfield University-based projects  
For these projects, send completed applications to Dr Stephen Hallett
Characterising and exploring landscape resilience through big data and visualisation technologies
Landscape is the arena in which natural capital (providing ‘supporting’, ‘provisioning’, ‘regulating’ and ‘cultural’ ecosystem goods and services) interacts with elements of the other four ‘capital’s’ to create the real places that people inhabit, derive benefits from and care about. Throughout time, landscapes change as a result of natural processes but the rate of change is now orders of magnitudes greater due to anthropogenic activity. Thus key questions facing those organisations involved in landscape management and policy include:

  • is the rate of landscape change taking place today sustainable in terms of conserving the diversity of character and functions? and critically,
  • how resilient are England’s landscapes to change?

Natural England is one such organisation, responsible for delivering the Government Agenda in this field. Conservation 21 – Natural England’s conservation strategy for the 21st Century, places emphasis on ‘resilient landscapes’. Significantly they argue that ‘resilient’ landscapes must be both ecologically and culturally resilient (implying culturally valued/supported/voted-for etc.), highlighting that landscapes lacking cultural resilience are unlikely to be ecologically resilient in the long term.

Working in partnership with Natural England’s Strategy Team, this exciting studentship will investigate the use of big data relating to natural and social sciences, together with, for example Virtual Reality visualisation and big data techniques, to provide an holistic, integrated analysis of ecosystem service provision as experienced through society’s perception of the changing landscapes around them and in a way that secures assessment of ecological and cultural aspects of the management of the natural environment equally. This will then enable development of management and intervention aimed at enhancing natural capital, ecosystem goods and services in their cultural context, by testing different scenarios. The studentship will be based in the Cranfield Institute for Resilient Futures and make extensive use of the NERC funded Ecosystem Services Databank and Visualisation for Terrestrial Informatics Laboratory, which includes a portable Virtalis stereo VR system and GeoVisionary software.

We are seeking a highly motivated candidate with an interest and aptitude for transdisciplinary environmental research, particularly geographical information systems/spatial analysis, policy, decision-making and social sciences. The candidate will be a member of the NERC/ESRC DREAM Centre for Doctoral Training and will receive excellent support in terms of training and mentoring.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level of equivalent in subjects such as Geography, Environmental Science, Ecology, Computing or Psychology. We are particularly interested in applicants with an interest in pursuing novel transdisciplinary environmental research.

For further details: Please contact Dr Simon Jude:

Email: s.jude@cranfield.ac.uk
Supervisory panel: Dr Simon Jude (Cranfield) and further details on Simon; Tim Brewer (Cranfield).

The project will involve working closely with Natural England, and there may be an opportunity for a secondment there during the research.

Aviation’s web: increasing the energy and environmental efficiency of the global aviation network
Global reductions in overall CO2 use were agreed under the 2015 Paris Agreement with the United Nations Framework Convention on Climate Change. Aviation is responsible for 2-3% of global, man-made CO2 emissions. It is currently growing at over 4% / year and is forecast to continue growing at a similar rate in the foreseeable future, with total aviation emissions doubling or tripling by 2050. Energy efficiency gains from new technologies have largely been realised with future improvements in efficiency likely to be <1%/annum. In response the recent international industry agreement on future CO2 emissions assumes that CO2-offsetting measures will be found for over 50% of the anticipated. This agreement does not cover operations and, consequently, one of the primary opportunities for reducing the impact of global aviation is being overlooked. There is thus considerable scope for improving the environmental impact of aviation and reducing the risk of environmental damage to society.

This project will analyse the global aviation network in order to quantify the main sources of the inefficiencies and to identify how improvements can be made most effectively on a 5-25 year timescale. Issues which will be addressed include traffic management, network limitations (airport location, runway length, terminal capacity). It will involve five main areas:

  1. model development, extending an existing simple model to assess all flights, allow for system optimisation and including a module to assess the impact on atmospheric CO2 and radiative forcing;
  2. collation of data sets, including real flight data from the global aviation network and airport specifications;
  3. analysis of overall aviation system, identifying and quantifying the main causes of current inefficiencies;
  4. investigation of options to improve efficiencies; and
  5. development of a strategy with prioritised measures.

This project will appeal to individuals who like the challenge of analysing all components of a system together and who want to see ‘the big picture’. Good interpersonal skills are required as the project will involve liaising with national and international industry and regulatory bodies. There is scope for an extended placement with such a group in order to perform more detailed studies with the assistance of practitioners.

About you: The work will require proficiency in computing and handling of large data sets and an ability to look at all aspects of a complicated system. Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Computer Science, Environmental Science, Aeronautics, Engineering, Geography or Natural Sciences.

For further details: Please contact Professor Neil Harris:

Email: neil.harris@cranfield.ac.uk
Telephone: +44 (0) +44 1234 75 8155
Supervisory panel: Professor Neil Harris (Cranfield); Dr Nazmiye Ozkan (Cranfield)

Consultant advisor: Professor Ian Poll, Emeritus Professor of Aeronautics (Cranfield)

BACTERIA: Big dAta Communication sTrategiEs foR bIoAerosols
Biological air pollution (bioaerosols) are airborne microorganisms, particularly fungi and bacteria. Bioaerosols from composting facilities have the potential to cause health impacts and are regulated by the Environment Agency. People living near composting facilities are concerned about the impacts on their health. Current monitoring methods use spot measurements and so only provide an indication of concentrations for the particular short-term measurement period. New and novel methods for monitoring bioaerosols are being tested. These newly emerging measurement techniques have the potential to radically increase the amount and extent of data collected on bioaerosol.

This exciting project provides the opportunity to work with the Environment Agency, to explore innovative methods of collating, analysing and interpreting different sources of bioaerosol data to produce new insights and risk maps. These will provide new insights into how composting can be managed for the benefit of local citizens. This project will also work with interested parties, such as the Environment Agency, local authorities and businesses, to understand the perceptions and opinions of impacts from composting facilities, for example, whether they are actively supportive of composting, unaware and uninterested in its developments and/or significantly opposed to it? All the results will be used to create a toolkit to communicate the risks of bioaerosols, focussing on how the uncertainties are explained and managed. In addition to working with the Environment Agency, this PhD student will work within a supportive team of researchers working on waste management and bioaerosol science.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Geography or Natural Science. Students from social science backgrounds with statistics or similar skills will also be considered.

For further details: Please contact Dr Gill Drew:

Email: g.h.drew@cranfield.ac.uk
Telephone: +44 (0) 1234 750111 x2792
Supervisory panel: Dr Gill Drew (Cranfield); Dr Kenisha Garnett (Cranfield); Dr Iq Mead (Cranfield)

Industrial partners: Rob Kinnersley (EA)  rob.kinnersley@environment-agency.gov, and Kerry Walsh (EA)  kerry.walsh@environment-agency.gov.uk

Understanding Air Quality and Climate Monitoring Scales: Merging Networks.
This project is based on researching air quality and greenhouse gas measurement. Specifically understanding the scales at which these measurements are made and how to optimise these measurements in the future. The emergence of relatively low cost miniaturised environmental sensor technologies has led to a huge increase in available and potential data. Observational studies can be undertaken at higher resolutions and by a range of stakeholders from national governmental compliance monitoring networks to schools. These studies can be at a range of qualities and with a range of calibration or validation data associated with them. Reporting can vary from seconds to hourly (or more) and over a range of periods (a few hours to years of regular data). These new sensing capabilities allow us to probe the environment (especially the urban environment) at previously unachievable scales.

This work will investigate effective scales of measurement in future networks. Both for long term regulatory monitoring and targeted campaigns. This work would develop optimised network operation and bridge the gap between mixed mode and quality network outputs and large footprint model or remote sensing products. It will gauge thresholds for improving returns when defining or altering network outputs and integrating with model or remote sensing products.

This has particular relevance to the changing economic landscape across sub Saharan Africa where existing cities are expanding, in some case to become global megacities, and new cities and urban centres are emerging. In the case of expanding African urban centres it is often the case that sources (e.g. transport, industry, power generation) are based on an existing infrastructure not suitable for scaling with population. There is little monitoring being undertaken (or in some cases none) and so effective mitigation plans are both difficult to effectively formulate and quantitatively assess. Opportunities exist to both improve the situation in expanding large cities as well as build in monitoring and control in emerging urban centres.

This research aims to improve the integration and understanding of scales of measurements in the atmospheric science “big data” challenges of changing global air pollution monitoring and quantifying urban emissions from existing and emerging megacities. This work will explore improving and expanding measurement capability to fill current gaps and will focus on integration of low cost, high density sensing systems with existing relatively sparse monitoring especially in areas with limited monitoring coverage such as in sub Saharan Africa.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Earth Science, Atmospheric Chemistry, Computer Science or Natural Sciences.

For further details: Please contact Dr Iq Mead (Cranfield):

Email: Iq.Mead@cranfield.ac.uk
Telephone: +44 (0) 1234 75 8106
Supervisory panel: Dr Iq Mead (Cranfield); Professor Neil Harris (Cranfield); Prof Rod Jones, (Department of Chemistry, Cambridge University) rlj1001@cam.ac.uk

Industrial partner: Rob Kinnersley (EA)  rob.kinnersley@environment-agency.gov

Development of an in-field diagnostic tool to measure nutrients to suit crop demand and minimise risk to the environment
Sustainable intensification of agriculture is a vital approach to maintain the balance between providing food for a growing global population and preserving soil as a valuable resource. Currently the state of arable intensive land is poor and the use of organic amendments such as compost, farm yard manure and other agricultural residues can provide soil organic matter required to restore soil health. Organic amendments also contain nutrients needed by crops but its availability needs to be determined accurately to meet crop demands. At the moment, samples need to be sent to laboratories for nutrient analysis and farmers need to consider it before accurately applying it to land. However in practice this does not happen and farmers bulk apply the organic amendments without fully considering its nutrient content. Bulk application of organic amendments whilst can build soil organic matter, its nutrient when becomes available and in excess of crop demands can pose a risk to contaminate the environment. A solution to this challenge would be development of an in-field diagnostic tool that can be used to determine the nutrient content of organic amendments in an accurate and precise manner.

This PhD opportunity offers an exciting and challenging offer to a suitable candidate to develop an in-field diagnostic tool. At Cranfield University, there has been some initial proof of concept work being developed which will under pin this project. In this project there will be contribution from two industrial sponsors (AKVO – www.akvo.org, and the World Vegetable Centre – www.avrdc.org) who have access to several thousands of field sites in Vietnam and Cambodia where data from soil and organic amendment samples will be collected and the use of in-field diagnostic tool will be developed and validated. The principles behind development of this tool is to produce a mobile phone App that can be used to determine the nutrient content of organic amendments in developing countries where access to laboratories are limited. The aspiration is to develop, use and validate this tool from a large dataset to minimise risk from over applying organic amendments.

The candidate is expected to be highly skilled in numeracy due to handling large datasets from field sites and knowledgeable about soil fertility and chemistry. The candidate will be given excellent support in terms of training in handling large datasets and numerical analysis, analytical method for nutrient analysis, risk analysis and other bespoke MSc modules. The candidate will be part of a Doctoral Training Centre at Cranfield University which is a virtual hub that provides access to core and subsidiary training needed throughout this project.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level of equivalent in subjects such as Soil Science, Environmental Science, Agronomy, Mathematics or Chemistry. Applicants with experience in carrying out large datasets and computer programming languages will be an added advantage. It will be desirable for candidates to have an MSc in the above subjects.

For further details: Please contact Dr Ruben Sakrabani (Cranfield):

Email: r.sakrabani@cranfield.ac.uk
Telephone: +44 (0) 1234 75 8106
Supervisory panel: Dr Ruben Sakrabani (Cranfield); Dr Stephen Hallett (Cranfield) (see also for Further details)

Industrial partner: Mr Joy Ghosh (AKVO)  joy@akvo.org, and Mr Stuart Brown (World Vegetable Centre)  stuart.brown@worldveg.org

Cloud based imagery services for understanding landscape change to support opium monitoring in Afghanistan
Information on illicit poppy cultivation in Afghanistan is of critical importance to the opium monitoring programme of the United Nations Office on Drugs and Crime (UNODC). The pattern of cultivation is constantly evolving because of environmental pressures, such as water availability, and social and economic drivers related to counter narcotics activity. Remote sensing already plays a key role in gathering information on the area of opium cultivation and its spatial distribution. The shift to cloud computing opens up exciting possibilities for extracting new information from the huge amounts of satellite data from long-term earth observation programmes. You will test the hypothesis that inter-annual and intra-seasonal changes in vegetation growth cycles are predictors of poppy cultivation risk. This will involve using emerging cloud based technologies for processing image data into accurate and timely information on vegetation dynamics relating to opium cultivation.

The research will be conducted in collaboration with the UNODC. You will have access to the full range of training programmes within DREAM and the Doctoral Training Centre of the University to support your research and personal development.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Engineering, Geography or Natural Sciences.

For further details: Please contact Dr Toby Waine (Cranfield):

Email: t.w.waine@cranfield.ac.uk
Telephone: +44 (0) 1234 750111 x 2770
Supervisory panel: Dr Toby Waine (Cranfield); Dr Dan Simms (Cranfield)

Industrial partner: United Nations Office on Drugs and Crime, Vienna

Newcastle University-based projects  
For these projects, send completed applications to Dr Stuart Barr
Massive multi-agent simulation of environmental risks to interdependent infrastructure
This PhD will develop a new agent-based modelling approach to represent organisational and social interdependencies between infrastructure systems.

Analysis of infrastructure interdependencies has focused on the physical infrastructure networks, for example the supervisors have been looking at interdependent infrastructure systems and how failure of one physical system (e.g. energy network) can lead to knock on failure of other (e.g. loss of power to pump and distribute water supply), or vice versa (e.g. water is required for cooling power stations).

This work has highlighted some fascinating properties of interdependent networks – including how the nature of inter-connections can increase their overall vulnerability. However, decisions made by individuals and organisations who operate, manage and use these physical systems are just as important, if not more so, in mediating interdependent interactions. To understand the role of these agents, will require a new approach able to capture and represent social systems. This PhD will further develop a new coupled simulation model that combines an agent based model to represent interactions and decisions made about the operation of infrastructure, with simulation of the physical systems themselves. The sheer number of agents involved necessitates a cloud-based approach for large scale infrastructure simulation and so the developed approach must be computationally scaleable. The model will be tested on real infrastructure examples and used to identify strategies to improve the management and operation of infrastructure.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Engineering, Geography or Natural Sciences.

For further details: Please contact Professor Richard Dawson (Newcastle):

Email: richard.dawson@newcastle.ac.uk
Telephone: +44 (0) 191 208 6618
Supervisory panel: Professor Richard Dawson (Newcastle), Dr Sarah Dunn (Newcastle)

Environmental risks to global resource flows
Extreme weather events, at a range of scales, have led to disruption of resource movements. These resources such as water, food, materials and goods are vital to the safety, health and livelihoods of individuals and communities. The 2011 Floods in in the Chao Praya River Basin in Thailand disrupted global computer manufacturing as about 25% of all hard drives in the world are manufactured in the region. Similarly, the 2011 East Coast of Japan tsunami reduced the production capacity of Japanese industry, with a slow of materials and supplies to downstream industries – including car assembly plants in the Northeast of England.

The scale of these global impacts is being driven by increasing interdependencies across infrastructures and supply chains. This complexity poses substantial challenges for those seeking to move resources, and environmental risk managers aiming to reduce the disruption to resource movements before, during and after extreme events.

This PhD will produce a quantitative resource model that simulates relationships of supply and demand of resources within a global scale spatial network model. The impacts of a spatial hazard, such as a flood, on these supply chains can be evaluated. The work builds on successful demonstration of this approach at the intra-urban scale and will take advantage of large data sets for supply chains, trade flows, environmental hazards as well as new computational capabilities to analyse large spatial networks.

The model will be used to provide an environmental risk assessment to global resource flows, identify priority supply chain and resource flow risks, and test the effectiveness of measures to increase their resilience.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Engineering, Geography or Natural Sciences

For further details: Please contact Professor Richard Dawson (Newcastle):

Email: richard.dawson@newcastle.ac.uk
Telephone: +44 (0) 191 208 6618
Supervisory panel: Professor Richard Dawson (Newcastle), Dr Stuart Barr

High resolution modelling of real-world floods – models, forecasts and uncertainties
This project seeks to identify, quantify and communicate uncertainties associated with high-resolution hydrodynamic flood simulations in order to assess their added value over current operational models for flood forecasting. The work will be done in collaboration with the Scottish Environment Protection Agency (SEPA) and focus on recent Scottish floods as case studies.

Climate change has increased the frequency and severity of extreme weather events around the globe. Heavy floods have been observed over the past decade due to a rise in rainfall intensities across a range of timescales. In the UK, prolonged rainfall during winter has caused fluvial and groundwater flooding, while intense convective rainfall events during summer have caused surface water flooding. Therefore, there is a need for better flood forecasting to enable emergency responders to act more effectively.

Cutting-edge flood modelling technologies are currently not used by flood forecasting centres in the UK. There are several barriers to this, however, a lack of understanding about the uncertainties involved in high-resolution flood modelling is key as it prevents a detailed assessment of the benefits and limitations of moving to a more complex modelling system. With climate change increasing the frequency and intensity of flooding across the UK, we need to be using the most useful modelling tools available to enable emergency responders to act as effectively as possible.

A physically-based hydrological model – SHETRAN (http://research.ncl.ac.uk/shetran/index.htm) will be used in conjunction with a High-Performance Integrated hydrodynamic Modelling System (HiPIMS) (Liang and Smith, 2015) to simulate the catchment flooding processes following intense rainfall. This shock-capturing hydrodynamic modelling system will take inputs from historic forecasts of extreme rainfall, national datasets of topography, soil, geology and land cover. A full scale uncertainty analysis will be conducted by running large sets of simulations on the cloud. Then, working closely with SEPA, strategies to reduce uncertainties will be developed and assessed.

Depending on the background of the student, training will be provided through formal courses in hydrology, hydraulics, modelling, climate change, statistics, and programming. The student would expect to gain skills in fundamental modelling techniques, as well as experience in current issues in flood risk assessment. The student will benefit from 2 secondments to SEPA and one visit to the USA National Center for Atmospheric Research (NCAR) where he/she will attend one of the annual meetings of the Engineering for Climate Extremes Partnership (ECEP), a multi-stakeholder network.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Engineering or Natural Sciences with at least A-level mathematics (or equivalent) and a strong aptitude for programming.

For further details: Please contact Professor Hayley Fowler (Newcastle):

Email: hayley.fowler@ncl.ac.uk
Telephone: +44 (0) 191 208 7113
Supervisory panel: Professor Hayley Fowler (Newcastle); Dr Elizabeth Lewis (Newcastle); Dr Selma Guerreiro (Newcastle); Professor Qiuhua Liang (Newcastle)

Industrial partner: Louise Parry (SEPA)  louise.parry@sepa.org.uk

Extreme rainfall forecasting: new statistical simulation and Big Data methods for making sense of rainfall radar and rain gauges
Understanding and forecasting rainfall, especially from thunderstorms, and the consequent risk of flooding in cities is a long standing and important problem in both practical and research terms. Rainfall radar is our most powerful observational tool giving real time spatial images of rainfall, but unfortunately has major issues with accuracy and reliability. There are many sources of error in the calibration and interpretation of radar data, as the return signal can be the product of reflection from multiple rainfall targets, buildings and atmospheric phenomena.

This project will take a fresh view of making better use of radar data, using new mathematical and statistical approaches, empowered by Big Data computational methods. A new high resolution rainfall radar is operating in Newcastle for use in flood risk studies in combination with a dense telemetered rain gauge network across the city and surrounding area. This project will develop and apply new statistical simulation approaches to using the radar and rain gauge data effectively, recognising the usefulness and importance of both the space and time properties of the rainfall fields, together with large volumes of data from ground-based rain gauges.

The student will undertake a varied research programme, which could include: meteorology of rainfall, physics of radar signals, statistics of time series and spatial dependence of (pseudo-)random fields, mathematics of conditional simulation, and use of Cloud technology for data processing and computational simulation. Finally, the student will liaise with operational engineers and designers in the best use of their results in real time and long term assessments of flood risk and intervention in cities.

About you: A minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Physics, Engineering or Natural Sciences. Quantitative background, ideally including programming (e.g. Python, C++), geospatial data analysis, statistical analysis (e.g. R).

For further details: Please contact Professor Chris Kilsby (Newcastle):

Email: chris.kilsby@ncl.ac.uk
Telephone: +44 (0) 191 208 5614
Supervisory panel: Professor Chris Kilsby (Newcastle); Professor Andras Bardossy (Newcastle)

Industrial partner: Mark Dutton (Environmental Measurements Ltd. – EML)

Capturing Tsunamis and Storm Surges: Coupling the Human and Natural Systems through Games Technology
There have been a number of recent coastal inundation flood events in which the societal responses have proved inadequate, for example New Orleans response to Hurricane Katrina. There is concern that due to an intensification of the hydrological cycle caused by climate change, coupled with population growth and urbanisation, such devastating events have the potential to become more frequent in the future. An important question that needs to be addressed is: How can preparedness and response be improved to reduce the impacts? There is a need to study the reciprocal feedbacks between the natural and human systems to gain insight to address this question.

Advances in hydrodynamic modelling allow the physical processes associated with coastal inundation to be described in fine detail, at the scale of the individual buildings and infrastructure. However, the actions of the human component are much less well understood, for example: What motivates citizens to adhere to warnings? Serious gaming interfaces are becoming more widely used in exploring crisis management, allowing real-time, interactive, and highly realistic environments for advanced concept development, experimentation and training. Additionally, Big Data streams (e.g. Twitter) provide a means of understanding and capturing human motivations and behaviours both prior, during and immediately after an event. It is proposed that combining these advances in computation, gaming and Big Data can improve understanding to reduce societal impacts. This PhD will combine an existing high-performance hydrodynamic model with an agent-based model within a coupled human and natural system framework to explore historical case studies. The student will benefit from both expertise at Newcastle University and our partners at Kyoto University, Japan.

The studentship will be funded through a new Centre for Doctoral Training, “Data, Risk And Environmental Analytical Methods”, that combines training in risk mitigation science with cutting-edge big data interpretation across the environmental sciences.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Computer Science, Civil Engineering, Geography or Natural Sciences. They should have a strong background computer programming and ideally have experience in flood risk management.

For further details: Please contact Professor Qiuhua Liang (Newcastle):

Email: qiuhua.liang@ncl.ac.uk
Telephone: +44 (0) 191 208 6413
Supervisory panel: Professor Qiuhua Liang (Newcastle); Dr Greg O’Donnell (Newcastle); Dr Graham Morgan (Newcastle)

Earth observation for UK-wide flood infrastructure risk management
Flooding is considered by the UK Government to be one of the top risks to the nation, the recent UK Climate Change Risk Assessment identifying flooding as the uppermost environmental risk to the UK over the next century. Since the 2008 Pitt Review there has been significant progress in aspects of flood risk management, however subsequent events have repeatedly highlighted failings in UK flood resilience, and in particular the limitations of existing natural and manmade flood and coastal risk management infrastructure. The effective design and operation of such assets is predicated on accurate and reliable information. New technologies for observation and sensing have made possible a next generation of data streams that can characterise flood assets. Their exploitation necessitates a top-down framework capable of fusing and analysing multiple data sources to characterise asset condition and inform better understanding of flood resilience.

The aim of this project is to transform the reliability of flood defence condition characterisation by validating and integrating data from new and existing earth observation sources. In particular, the project will exploit the latest developments from ESA’s Copernicus programme, notably the Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) missions, to augment existing national datasets from the Environment Agency and Ordnance Survey, and determine potential vulnerability in flood asset condition. Enhanced remote sensing capability of vulnerable assets will also be demonstrated in order to help mitigate flood risk. Seizing the opportunity afforded by contemporary developments in big data processing, notably cloud computing, the studentship will demonstrate exemplar flood defence observation and condition characterisation at the national scale.

In addition to the DREAM cohort training plan, a bespoke local training package will be established for the study period. Exact details of the training to be received will be dependent on the student’s skillset and prior experience, but the successful candidate can expect to benefit from tuition in high performance computing via engagement with the Newcastle EPSRC CDT on “Cloud Computing for Big Data”. This will include the modules “Programming for Big Data”, “Cloud Computing” and “Big Data Analytics”. Numerical skills will be provided by the module “Quantitative methods for engineers”. The student will also attend the NERC COMET InSAR Training Workshop which previous Newcastle Earth Observation Laboratory (NEO-Lab) students at Newcastle have found an excellent introduction to satellite InSAR data. Further course selection from a broad portfolio of computing, geomatics and water resource engineering modules will also be possible.

About you: Applicants should hold a minimum of a UK Honours degree at 2:1 level in a subject such as Computer Science, Geomatics, Civil Engineering or Mathematics. Experience of computer programming and its application to spatio-temporal data would be desirable.

For further details: Please contact Professor John Mills (Newcastle):

Email: jon.mills@ncl.ac.uk
Telephone: ++44 (0)191 20 85393
Supervisory panel: Professor John Mills (Newcastle); Professor Zhenhong Li (Newcastle); Dr Paolo Missier (Newcastle)

Industrial partner: Jeremy Morley (Ordnance Survey)  jeremy.morley@os.uk

Preserving Privacy for Urban Data in the Internet of Things
Emerging availability (and varying complexity and types) of Internet of Things (IoT) devices, along with large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fueling the development of critical next-generation services in a variety of urban analytics application domains (e.g. smart grids, disaster management, air pollution monitoring, transportation and water management, etc). For example, Newcastle University is investing over £4 million in Urban Observatory program over the next five years and already has the largest set of openly accessible urban sensing data available in the UK. Over 700 sensors have been deployed already and many more are in the pipeline (http://uoweb1.ncl.ac.uk).

The significant increase in the types of IoT devices is also fueled by market forces, as many vendors are now embedding network capability with support for standard protocols (e.g. HTTP/REST) within their products, which were initially stand-alone. Understanding how data from such devices can be more efficiently analysed while preserving the privacy of the data producers remains a challenge.

For example, a key aspect of understanding complex urban systems at all scales is being able to accurately monitor the flows of people through the system. Data is being made available to the Newcastle Urban Observatory from pervasive city wide wireless access points and mobile phone cell registration data (The observatory acquires this data in partnership with Newcastle City Council from BT who are the service provider for city wireless). Similarly, at the building scale, sensing movement and activity is key to understanding how that building operates. Whilst this data is anonymised at its source and subject to a time delay there are still opportunities to access and exploit this data for criminal purposes, including identifying individuals and their movements.

This unprecedented growth in the type and capability of IoT devices, leads to potential challenges at the scale of the whole Internet. We must therefore develop holistic privacy preserving data processing models and methods capable of supporting next generation of urban analytics applications that make more efficient use of such IoT devices. This project is an attempt to do this by taking advantage of edge computing resources in combination with cloud computing systems. The research will be conducted in Computer Science through a series of pilot studies fusing privacy preserving models and methods with big data programming models (e.g., batch processing, stream processing, SQL, NoSQL) with real world urban analytics problems and thereby understanding and developing the requirement of the future IoT systems for managing and mitigating risks in urban environments.

About you: Applicants should have an honours degree in Computing Science or Engineering / Natural Sciences with a strong interest in IT and Computing. Candidates should have understanding of modelling, database systems and programming.

For further details: Please contact Dr Raj Ranjan (Newcastle):

Email: raj.ranjan@ncl.ac.uk
Supervisory panel: Dr Raj Ranjan (Newcastle); Dr Chang Liu (Newcastle)

Big data real-time online analysis of urban flooding impact on traffic flows

CCTV image from Newcastle’s Chillingham Road (A188).

Flooding is one of the major natural hazards facing by the UK. Cities are experiencing increasing surface water flooding due to intense rainfalls, leading to severe disruption, damage to property and infrastructure, and even loss of life. During the floods in 2007, over 35,000 homes and businesses were affected by surface water flooding. It is estimated that the cost of surface water flooding is as high as £2.2bn annually in the UK. New tools are required to improve the understanding of such hazards and their impacts, so that proper mitigation measurements can be taken.

Traffic on Newcastle’s Great North Road (A63) on Thursday 28th June 2012, compared to previous traffic flow (vehicles/hour) on the same day over the previous year. The rainfall started at approximately 3pm.

This project will investigate an urban flooding online real time analysis platform using big data for warning and decision support to manage risks on traffic flows, including road and crowd/pedestrian traffic. This involves a start-to-end analytical and computational framework that rigorously evaluates and integrates real-time data and information, e.g. CCTV, Traffic Detector, and social media. A scalable open source data management platform for the continuous acquisition, storage and indexing of the real-time data feeds will be developed. The spatial explicit data will be processed online to extract indications of moving patterns resulting from the hazard. In the case of urban traffic flow, numbers of vehicles and people and their locations and travelling directions will be extracted in order to evaluate and improve understanding on how urban flood impacts on traffic and population movements. It will facilitate the perception of urban flooding risks, enable the communication of end-users / traffic managers with the public commuters, and support timely decision-making and mitigation of cascade effects.

General workflow of the project

The PhD student will work closely with the Newcastle Urban Observatory, which is a ground-breaking project to monitor our city at multiple scales and provide long term data storage for monitoring. The student will build the online data processing and analysis platform together with the UO, expanding the system for real time information visualization and risk assessment. Also, the student will collaborate with the Newcastle EPSRC CDT Cloud Computing for Big Data to build an efficient computing capacity to handle large amount data in real-time. Tyne & Wear UTMC, who will provide CCTV streams, expertise, and feedback during the whole lifetime of the project, will advise this PhD project. Outcomes of the project will be adopted by UTMC for real world testing and simulation.

To help the career development of the PhD student trainings are provided throughout the project. The student will have access to the training in both Geospatial Science and Computer Science. Modules such as ‘Geospatial Algorithms’, ‘GIS Fundamentals’ and ‘Image Processing and Machine Vision’ are provided by Newcastle University. Basic knowledge of geo-visualization and web-GIS, and pattern recognition will be obtained. The student will also have chance to attend the annual BMVA summer school to strengthen skills in machine learning and computer vision. In addition, the student can choose to attend trainings from the Newcastle EPSRC CDT Big Data & Cloud Computing. Modules such as ‘Programming for Big Data’, ‘Cloud Computing’ and ‘Big data analytics and Time series analysis’ are provided. Apart from training courses, the student will also benefit from DREAM workshops and seminars held in our interdisciplinary groups.

About you: Applicants should hold a minimum of a UK Honours degree at 2:1 level in a subject such as Computer Science, Geographical Information Science / Geomatics, Environmental Science, Engineering. Experience of programming in Computer Vision or Image Processing and its application to environmental and/or spatial data is highly desirable.

For further details: Please contact Dr Wen Xiao (Newcastle):

Email: wen.xiao@ncl.ac.uk
Supervisory panel: Dr Wen Xiao (Newcastle); Philip James (Newcastle); Dr Raj Ranjan (Newcastle)

Cambridge University-based projects  
For these projects, send completed applications to Dr Mike Bithell
Vegetation assessment using machine learning techniques on multi-(hyper-)spectral data
It is estimated that the world’s food demand will increase by 70 % by 2050. Therefore, resources need to be used as efficiently and effectively as possible. Risks to crops need to be mitigated. Surprisingly, plants are very communicative about their state of health. They do so with the light reflected by their leaves. Most of this takes place in the part of the spectrum invisible to the human eye, and often long before the effect becomes visible to humans.

Terrestrial, airborne and high resolution satellite spectral imaging systems can enable effective and timely crop monitoring. Once a risk has been identified, it can be mitigated, for example by irrigation, fertilizer application or disease control measures. Timely intervention is of the essence. For large, industrialized farming, the monitoring of fields needs to be automated, with alerts when the plants show signs of stress. While at the other end of the scale, it is important for small holders to optimize their yield by using information and knowledge successfully.
The project aims to:

  1. develop an extensible, framework for analysing data incorporating machine learning and human expertise,
  2. using 1), build a database of spectral signatures of different plant species, accounting for plant life cycles and health status, and
  3. use 1) and 2) to provide near real-time summary information about crop status to support on-the-ground decision-making.

The candidate will be placed within the Centre for Scientific Computing (http://www.csc.cam.ac.uk/) at the Maxwell Centre which is part of the Cavendish Physics Laboratorium in Cambridge. There will also be interaction with scientists at the Department of Plant Sciences. The Centre for Scientific Computing offers many training opportunities within the area of High Performance Computing, Numerical Analysis and Machine Learning, since it hosts the MPhil in Scientific Computing as well as the EPSRC Centre for Doctoral Training in Computational Methods for Materials Science.

Funding: UK nationals and EU nationals who have been settled or ordinarily resident in the UK for 3 years will receive tuition fees and a maintenance grant from the NERC CDT fund. Other EU nationals will have fees paid.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Mathematics, Environmental Science, Computer Science, Engineering, Geography or Natural Sciences.

For further details: To apply in the first instance please contact Anita Faul by email, as below, with a CV to discuss the application. Successful applicants will be invited to apply through the University of Cambridge’s Graduate Admissions Office (http://www.graduate.study.cam.ac.uk/) and the NERC CDT (http://www.dream-cdt.ac.uk/) by 13th March 2017.

For further details: In the first instance please contact Dr Anita Faul (Cambridge) with a CV to discuss the application:

Email: acf22@cam.ac.uk
Telephone: +44 (0)1223 337273
Supervisory panel: Dr Anita Faul (Cambridge); Professor David Coomes (Cambridge); Carola-Bibiane Schönlieb (Cambridge)

Rumour Mill: measuring the veracity of rumours in the social media
With concerns about the rapid spread of newly emerging diseases such as A(H5N1), re-emerging diseases such as Ebola and the threat of bioterrorism there has been increasing attention on digital disease detection systems which can complement traditional indicator networks by detecting events on a global scale. Whilst ‘volume’ and ‘velocity’ in Big Data have been addressed in previous projects that analyse social media, the problem of ‘veracity’ (i.e. trustworthiness) remains a cutting-edge challenge. The goal of this research project (‘Rumour Mill) is to produce a robust computational model that can measure the likelihood of citizen’s assertions made about breaking news events. Key beneficiaries in the first instance will be the public health and animal health surveillance communities but the impact of the techniques are expected to be transferable to other domains.

Potential PhD students with a strong background in Machine Learning, Computational Linguistics or Artificial Intelligence are encouraged to explore creative solutions for veracity and to apply for this full-time three-year studentship funded by the NERC Centre for Doctoral Training in Data, Risk and Environmental Analytical Methods (DREAM). The studentship will start in the academic year 2016/17.

The successful applicant will work within an interdisciplinary team of researchers from Computational Linguistics to develop novel Natural Language Processing and Machine Learning techniques including models that exploit deep learning, reinforcement learning and other biologically inspired approaches.

The student will benefit from training and evaluation data being provided for a new community challenge task in rumour veracity detection but will be expected to construct disease risk-specific evaluation data during the course of the project. Motivated by the challenges outlined above the student will design and evaluate novel natural language understanding technologies in order to learn the most relevant clues for assessing rumour veracity. The exact scope of the project is open to discussion and will be shaped by the student’s individual strengths.

The PhD research will be undertaken at the Department of Theoretical and Applied Linguistics (DTAL) at the University of Cambridge. The successful candidate will be integrated into a friendly research-led team that conducts weekly meetings, publishes in top conferences and journals and with extensive inter-disciplinary collaborations.

Funding: UK nationals and EU nationals who have been settled or ordinarily resident in the UK for 3 years will receive tuition fees and a maintenance grant from the NERC CDT fund. Other EU nationals will have fees paid.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as
Environmental Science, Computer Science, Engineering, Geography or Natural Sciences

For further details: To apply in the first instance please contact Nigel Collier by email, as below, with a CV to discuss the application. Successful applicants will be invited to apply through the University of Cambridge’s Graduate Admissions Office (http://www.graduate.study.cam.ac.uk/courses/directory/mmalpdlng) and the NERC CDT (http://www.dream-cdt.ac.uk/) by 13th March 2017.

For further details: Please contact Dr Nigel Collier (Cambridge):

Email: nhc30@cam.ac.uk
Telephone: +44 (0)1223 7 67356
Supervisory panel: Dr Nigel Collier (Cambridge); Dr Anna Korhonen (Cambridge)

Industrial partner Jens Linge. Scientific/Technical Project Officer, Joint Research Centre of the European Commission  jens.linge@jrc.ec.europa.eu

Modelling ciliates and cilia as sentinel of environmental changes
The cilium is present in most mammalian cells and ciliates. Ciliates are an important group of protists (>> 3,500 species), common almost everywhere there is water. The cilium acts as an antenna and central processing unit that receives diverse signals from the extracellular environment, such as light, proteins, and mechanical stimuli.

The project will use multi omic data from Antarctic and temperate region living ciliates and available mammalian data on cilia to 1) build the first multi omic metabolic model of a ciliate, 2) integrate cilia mechanics with metabolic models; 3) calibrate ciliate models as environmental change responders on a number of scenarios; 4) identify analogous sensor functions dynamics of cilia in mammalian cells; understand linearity and non-linearity of physiological responses to network variations.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Engineering, Geography or Natural Sciences

For further details: To apply in the first instance please contact Pietro Lio’ by email, as below, with a CV to discuss the application. Successful applicants will be invited to apply through the University of Cambridge’s Graduate Admissions Office (http://www.graduate.study.cam.ac.uk/) and the NERC CDT (http://www.dream-cdt.ac.uk/) by 13th March 2017.

For further details: Please contact Dr Pietro Lio’ (Cambridge):

Email: Pietro.Lio@cl.cam.ac.uk
Telephone: +44 (0)1223-763604
Supervisory panel: Dr Pietro Lio’ (Cambridge); Dr Paolo Zuliani (Newcastle)


Birmingham University-based projects  
For these projects, send completed applications to Dr Emmanouil Tranos
Real-time integrated modelling of transport-related air pollution in urban street networks – risk assessment and policy evaluation
As cities become increasingly congested with growing traffic, the increase in transport-related emissions has raised concerns over the risk on human health and urban environment. Street canyons are hotspots of traffic-related air pollution, because at this spatial level significant variation of traffic volumes and the pollutant transport to receptors (i.e. exposed population) are both at a time scale of minutes. Many emission pollutants are chemically reactive, and fast reactions can take place at second-to-minute timescales to generate secondary pollutants. As a result, the variation of urban air quality can only be understood by considering the combined effects of fleet composition, traffic-produced turbulence, street and building geometries, and meteorological conditions in real time (i.e., a time scale of minutes). Fundamental scientific questions remain, for example, how to model real-time dynamics of the cause-and-effect process from transport activity to distribution of air pollution within street canyons, and how to account for both the heterogeneous distribution of air pollution across an urban street network and its variation in abundance with time. Answers to these questions are of utmost importance to better assess individual or population exposure and the potential risks on human health.

To model this cause-and-effect chain, an integrated modelling approach is built upon bridging research on three complex systems: (i) dynamical changes in travel demand, (ii) vehicle emissions, and (iii) dispersion of air pollutants in street canyons. This approach is thus, not only able to provide detailed indication of air quality in street canyons, but also can identify street sections with high volumes of vulnerable travellers, such as pedestrians and cyclists. These together contribute to a better quantification of potential health risks that could impose on a population in question. Furthermore, travel demand changes respond to different policies, such as speed limit and road pricing in Clean Air Zones; the effects propagate from varying traffic to emissions and cause the change in pollution concentrations. It is through the causes and effects reflected by this model chain that transport policies aiming at risk mitigation can be evaluated in regards to their effectiveness in reducing adverse impacts on human health and environment.

This project inherently deals with issues of Big Data, risk and mitigation in the integrated modelling approach. PhD students will receive related training from three aspects: 1) urban transport and air quality modelling and applications, 2) programming, computing and data handling, and 3) uncertainty and risk related analysis.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Geoinformatics, GIScience/Geocompuation, Transport Planning, Civil Engineering, Meteorology, Geography, Environmental Science, Computer/Data Science or Urban Planning. Applicants with skills in quantitative modelling or C/C++/Java programming are preferred.

For further details: Please contact Dr Zhaoya Gong (Birmingham):

Email: z.gong@bham.ac.uk
Telephone: +44(0)1214144240
Supervisory panel: Dr Zhaoya Gong (Birmingham); Dr Xiaoming Cai (Birmingham)

Industrial partners Anne Shaw (Birmingham City Council)  Anne.Shaw@birmingham.gov.uk

Mapping global peatland risk through a big data analysis
The regional climate provides the first order control on peatland ecohydrological function, driving surface mass and energy exchange. From these inputs, peatlands respond with a range of positive and negative ecohydrological feedback that have developed over millennia to regulate their hydrology and provide stable ecosystem function. However, the global climate is changing, with the greatest temperature increases being observed in high latitude regions where peatlands dominate. Despite climate acting as a first order control on peatland hydrological function, the assessment of peatland ecosystems has focussed on disconnected regional peatland studies from individual researchers or consortium within given local climate envelopes. This extensive global research effort has greatly enhanced our understanding of individual systems that have developed within the context of a given climate. However, this approach does not offer the capability to directly assess the core control of climate on peatland function and the feedbacks that regulate this global driver.

This project will tackle this global challenge through a unique, newly formed, global peatland big data network (PeatDataHub) that brings together the vast sources of data from peatland monitoring sites from across the globe. It will offer the unique opportunity to address how the global climate cycles, a superposition of varying climate signals of different intensities and phases, provides the overarching driver of peatland hydrology. It will address the extent to which peatlands evolve over time to regulate this climate signature to induce stable hydrological conditions that minimises the risk of ecosystem failure. Further it will provide an assessment of the extent to which this regulation is lost and climate signatures potentially enhanced with future shifts in regional climate envelopes; when there is a mismatch between the climate cycle and the peatland regulating system. The research will provide a driver to expand PeatDataHub into the peatland data repository that symbolises the future desires and demands for open access, freely accessible, open source big data science.

The research will integrate a range of statistical and modelling methods and approaches to explore the hydroclimatology of peatland ecosystems:

  1. Water table oscillations across the global distribution of peatlands will be characterised and clustered based upon their hydrologic wave form.
  2. The transition in the climate wave form to hydrological wave form will be assessed across the global data base. This will consider how such transfer functions are developed to regulate peatland ecohydrology and enhance system stability.
  3. The distribution of transfer functions will be incorporated within simple modelling frameworks to assess how a mismatch between transfer function and climate cycles or peatland type transforms the hydrological dynamics of the peatland systems and induces instability.
  4. Models of global peatland hydrological dynamics will be driven with future global climatic projections to assess the extent current system dynamics are transformed.

About you: Applicants should hold a minimum of an Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Mathematics, Statistics, Engineering, Geography or Natural Sciences.

For further details: Please contact Dr Nick Kettridge (Birmingham):

Email: n.kettridge@bham.ac.uk
Telephone: +44 (0)121 414 3575
Supervisory panel: Dr Nick Kettridge (Birmingham); Dr Geoffrey Parkin (Newcastle); Professor Joseph Holden (Birmingham)

Industrial partners Jonathan Walker (Moors for the Future)  Jonathan.Walker@peakdistrict.gov.uk

Urban traffic pollution - a big data approach
The transport of people and goods is key to the success of the modern city. Hence good transportation is a pre-requisite for a successful city. However, transport has a darker side in its generation of atmospheric pollutants such as nitrogen oxides and particulate matter. Ambient air pollution is a major risk factor for premature deaths, years of life lost, and years of life lost to disability. The cost of air pollution through loss of life and ability to work has a very detrimental impact upon the worldwide economy; in the UK this economic impact is estimated to be £54B per year (OECD). In particular air pollution is a key risk factor for cancers, lower respiratory infections, cardiovascular and cerebrovascular disease. Worldwide air pollution kills in excess of 7 million people each year (WHO). In the UK, the major emitters of the key air pollutant (PM and nitrogen oxides) are from vehicle use. The Department of Health’s Committee on the Medical Effects of air Pollutants (COMEAP) estimate that long term exposure to air pollutants result in 29,000 deaths in the UK each year and is instrumental in triggering more than 200,000 heart attacks.

This project will investigate the relationships between traffic fleet composition and movement within the city of Birmingham, UK, and how this affects the spatial distribution and concentration of air pollutants. Telematics data will provide real time data on fine grained vehicle flow in the city allowing an understanding of precise acceleration and braking behaviour and behavioural aspects of fine grained mobility.

The objectives of the project are as follows:

  1. Identify and combine all pre-existing big data streams of telematics, meteorology and air pollutants for the city of Birmingham.
  2. Through advanced analytical approaches, interrogate the dataset for causal links between driving conditions and behaviour with air pollution.
  3. Identify locations within the city where interventions, such as changing traffic light design, could lead to lowering air pollution.
  4. Disseminate the information to all relevant stakeholders through both traditional academic formats (scientific papers and international conferences) and by engaging directly with stakeholders including BCC.
  5. The project will work with the CASE partner ‘The Floow Limited’ who are world leading experts in telematics and the real time mobility understanding of the traffic fleet.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in a mathematical, physical sciences or engineering based subject such as physics, chemistry, mathematics, computer science or engineering. The student should have an aptitude for practical and computer based work. A background in computer programming would be most beneficial e.g. Python, R, Fortran.

For further details: Please contact Dr Francis Pope (Birmingham):

Email: f.pope@bham.ac.uk
Telephone: +44 (0)121 414 9067
Supervisory panel: Dr Francis Pope (Birmingham); Professor Lee Chapman (Birmingham)

Industrial partners Dr Sam Chapman (The Floow Limited)  sam@thefloow.com

Elucidating the impact of carbon dioxide enhancement on fungal and fungal-like plant pathogen dynamics

BiFOR ‘Free-Air Carbon Dioxide Enrichment’ (FACE) facility

This PhD project will investigate the effect of enhanced carbon dioxide (CO2) concentrations, caused by anthropogenic climate change, upon ecological networks within oak woodlands. It will characterise the response of ecological networks to CO2 enhancement by measuring species networks under both enhanced (550 ppm) and non-enhanced (ambient ca. 400 ppm) CO2 concentrations. This CO2 enhancement is highly likely to occur in the next 50-100 years.

Oak species dominate the UK’s lowland woods and, like many native trees, are under threat from emerging pests and disease, particularly oak processionary moth (OPM) and acute oak decline (AOD). These new pests and diseases act alongside more established environmental stresses that are often associated with climate change. They add to the constant pressure of established and endemic threats such as oak mildew and honey fungus.

To determine the interaction pathways between oak (and other woodland species), invertebrate pests and tree pathogens, the student will construct the most comprehensive and highly-resolved oak woodland ‘networks of ecological networks’ using state-of-the art molecular ‘community metagenomic approaches. These networks together with environmental data will then be applied in environmental modelling to predict long-term risks associated with tree pathogens.

BiFOR ‘Free-Air Carbon Dioxide Enrichment’ (FACE) facility

A ground-breaking ‘Free-Air Carbon Dioxide Enrichment’ (FACE) facility, currently being installed in the research forest (Mill Haft) of the University of Birmingham, will be used to investigate the effect of enhanced CO2 on these sensitive ecological networks. The Mill Haft site enables world-leading scientists to explore the forest thoroughly, taking measurements everywhere from deep within the soil to above the tree canopy. The FACE experiment, applied to oak and other woodland tree species, will provide a formidable addition to the Mill Haft site’s capability to probe the biosphere’s response to increases in atmospheric CO2.

About you: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Biological Sciences, Environmental Sciences, Geography or Natural Sciences. Enthusiasm for the project is of uppermost importance.

For further details: Please contact Dr Francis Pope (Birmingham):

Email: f.pope@bham.ac.uk
Telephone: +44 (0)121 414 9067
Supervisory panel: Dr Francis Pope (Birmingham); Dr Kerstin Voelz (Birmingham)

Upscaling UK Flood Defence Assets: Assessment and application of international flood defences in development of a holistic approach to UK fluvial flood risk management for future climate and land use scenarios
Innovative and proactive work is needed in order to prepare the UK for more significant flooding under future climate and land use scenarios. This research will provide new and tested techniques for sustainable fluvial flood mitigation. Working closely with, and providing recommendations to government partners such as the Environment Agency this work will drive the advancement and development of effective River Basin Management Plans supporting a holistic approach to UK flood mitigation. The project aims to develop a holistic strategy for UK flood defence assets in order to limit the impacts of fluvial flooding under future climate and land use scenarios.

Assessment of the success of international flood defences and their suitability in application to the UK system will be key to the project. The PhD will focus particularly on examples from The Netherlands. Here, the success of Dutch techniques such as the Polder system, Dykes and Dams, and “Room for the River” will be assessed. Application of Dutch water management techniques to the UK system will be considered. Through the use of computer modelling, map analysis, private/public/government and community engagement, adaptations to Dutch techniques will be developed so as to fit with UK hydrological processes and requirements.

Both soft and hard engineering techniques and community engagement will be considered and assessed equally. Through analysis, adaptation and modelling of international measures to limit fluvial flooding, the project will produce a flood defence strategy ready for application to the UK.

The project benefits from international collaboration and close contact with river management organisations like the Environment Agency in England and Rijkswaterstaat (the Ministry of Water and Infrastructure) in the Netherlands. Placements at both organisations are expected. The National Flood Forum will be involved to guide the community engagement aspects of this project.

About you: Applicants should hold a UK Honours Degree and an MSc at a minimum of 2:1 level or equivalent in subjects such as Hydrology, Water Management, Environmental Science, Computer Science, Engineering, Geography or Natural Sciences. Flood modelling experience and R programming skills are highly recommended.

For further details: Please contact Dr Anne F. Van Loon (Birmingham):

Email: a.f.vanloon@bham.ac.uk
Telephone: +44 (0) 121 414 2243
Supervisory panel: Dr Anne F. Van Loon (Birmingham); Dr Chris Bradley (Birmingham)

Risk of sudden tree death by water stress – BIG DATA for reducing uncertainties in forest hydrology
There is critical uncertainty about the drivers of severe water stress in UK woodlands, bearing the risk of reduced water use efficiency and even sudden tree death. The predicted increase in frequency and intensity of hydrological extremes for the UK presents a critical challenge for current and future forest water resource management that lacks suitable monitoring tools for understanding the dynamics of underground water resources and their response to event-based and seasonal drivers in rapidly changing land use and climate conditions, resulting in inadequate management, sudden tree death from water stress and significant economic losses and environmental impacts. With Rainfall extremes projected to increase in the UK, particularly during winter, and the prediction of hotter and drier summers, the environmental impacts of floods and droughts are expected to increase in severity. Knowledge of the temporal and spatial patterns of infiltration and soil water content (in particular in the root zone) is crucial to warrant a sustainable water resources management in forestry and ensure ecological water demands of forest ecosystems are met.

This project will build on pioneering the analysis of BIG DATA from the ongoing high-frequency and resolution, real-time soil moisture monitoring at the Mill Haft research site of BIFoR – the Birmingham Institute of Forest Research (http://www.birmingham.ac.uk/research/activity/bifor/index.aspx). It will utilize the previous development of a novel, distributed sensor network based on heat-pulse, Active Distributed Temperature Sensor (A-DTS) technology. This newly developed system enables real-time monitoring of soil moisture and temperature at unprecedented high spatial and temporal (1 second) resolutions for intermediate-sized spatial footprints (100-1000’s of meters). The A-DTS soil moisture network and its real-time interface and several terabyte of data archive will be used to analyse the short- to long-term hydrological (soil moisture, infiltration) responses to event-based, seasonal and supra-seasonal variability in meteorological impacts, with a particular focus on extremes such as storms and droughts.

In addition to training opportunities provided by the DREAM DTC, the project offers comprehensive training and networking opportunities via the School of Geography, Earth and Environmental Sciences graduate school. In addition to the interdisciplinary in house training programme, the project provides unique opportunities to benefit from the international training and career development opportunities of the HiFreq (Smart high-frequency environmental sensor networks for quantifying nonlinear hydrological process dynamics across spatial scales) H2020-PEOPLE-ITN lead by Prof. Krause and dedicated training in Forest Ecohydrology within the Birmingham Institute of Forest Research (BIFoR).

About you: Applicants should hold a minimum of an Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Mathematics, Statistics, Engineering, Geography or Natural Sciences.

For further details: Please contact Dr Stefan Krause (Birmingham):

Email: s.krause@bham.ac.uk
Telephone: +44 (0)121 414 5535
Supervisory panel: Dr Stefan Krause (Birmingham); Professor Hayley Fowler (Newcastle)

Measuring where ecosystems will go - thermodynamic assessment of stability and instability of peatlands
Peatlands represent a vital global carbon store that has accumulated over millennia. However, these complex environmental systems are facing a range of concurrent disturbances. Up until now our understanding of the risk of these carbon stocks has been explored through traditional environmental approaches. However, the laws of thermodynamics provide the drivers that underpin these traditional interpretations and potentially offer powerful new insights into the stability of peatland ecosystems and their future direction of response.

Simulations show hypothetical peatland profiles develop either to maximize vegetation productivity or minimize water loss by limiting evaporation. A global assessment of peat hydrophysical properties suggests real soil profiles attempt to balance the competing demands of carbon accumulation and water storage. Recognizing that peatland systems have such system priorities is crucial to the assessing, managing and mitigation the risk of peat carbon stocks to external pressures. However, the underlying cause of these differing states is uncertain. Further, the risk of peatlands transforming from one state to another is unknown. Whether these act as stable states, or whether there is a development trajectory over time from one functional form to another is not known. Simultaneously, the idea that peatland could form similar states based on their requirement to maximize or minimize entropy loss has been developed. The approach has the potential to provide the underlying control on the given peatland form and determine the direction and risk of potential system transition over time.

Research questions: Do the laws of thermodynamics dictate peatland function and inform risk assessment and mitigation strategies?

Proposed programme:

  1. Formulate entropy budget of undisturbed UK peatlands (ECN long term dataset, Moor House, upper Teesdale) to derive levels of thermodynamic equilibrium.
  2. Determine how peatland thermodynamic equilibrium is modified by land management practises and disturbance regimes utilizing flux tower measurements across a range of disturbed peatland systems, including 9 sites from the DEFRA lowland peat project.
  3. Determine how vegetation and tree canopies modify entropy loss through detailed evaporative investigations from Canadian research sites.
  4. Assess potential of thermodynamics to inform risk assessment and mitigation strategies through formulation of peatland entropy model that represents the direction of change in peatlands.

In addition to training provided by DREAM, and your integration within the Physical Geography group and Birmingham Institute of Forest Research, you will participate in Canadian wide academic and industry lead training with the HEAD3 research programmes. This collaboration will provide you with wide ranging hydrological, ecological, biogeochemical and meteorological skills in addition to statistical, modelling, data handling and project management skills.

About you: Applicants should hold a minimum of an Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Computer Science, Mathematics, Statistics, Engineering, Geography or Natural Sciences.

For further details: Please contact Dr Nick Ketteridge (Birmingham):

Email: n.kettridge@bham.ac.uk
Telephone: +44 (0)121 414 3575
Supervisory panel: Dr Nick Ketteridge (Birmingham); Dr Andrew D. Friend (Cambridge)

Advisor: Professor Fred Worrell, Durham University  Fred.worrall@durham.ac.uk. The project will also be able to draw upon a body of researchers from a wider Defra-sponsored Lowland Peat proejct

Estimating the risk of Antarctic ice shelf collapse using Bayesian nonparametric statistical modelling
Ice Shelf
Ice shelves comprise floating extensions of the inland ice of the East, West and Antarctic Peninsula ice sheets. They provide crucial buttressing forces holding back the flow of the ice sheets towards the sea, thus regulating rates of global sea-level rise. In recent years, ice shelves in the Antarctic Peninsula have been observed to substantially retreat and even catastrophically collapse. These major global change episodes have been linked to a variety of causal mechanisms, yet no single clear explanation has emerged, making physically-based forecasts of future change problematic.

This project will provide expertise and training in satellite remote sensing, expert elicitation, Bayesian methods and risk assessment to address the problem of ice shelf collapse. New satellite data (microwave and optical imagery) will be analysed to assess ice shelf retreat and collapse since 2010, placing these new observations in the context of the last half decade of observational ice shelf history. An expert elicitation exercise will quantitatively assess expert opinion of ice shelf collapse risk in the next 100 years.

These datasets will then be combined with existing environmental, geophysical and glaciological ‘Big Data’-sets in a Bayesian nonparametric statistical model framework to calculate the probabilities of ice shelf collapse risk during the next 100 years. The candidate will gain expertise and experience in glaciology, satellite remote sensing analysis, expert elicitation, and Bayesian numerical methods. This combination of skills is unique and in high demand and is expected to result in a number of high-impact outputs. The collapse timing estimates generated by this project may then be used by ice sheet modellers to more accurately forecast the future contributions of the Antarctic ice sheets to global sea-level rise.

About you: Candidates will normally hold relevant masters and first class or equivalent honours degrees in Physics, Mathematics, Computer Science, or numerate Geoscience disciplines (Geophysics, Earth Science, Physical Geography). Students with strong numerical and/or programming skills are particularly encouraged to apply.

For further details: Please contact Dr Nicholas Barrand (Birmingham):

Email: n.e.barrand@bham.ac.uk
Telephone: +44 (0)121 414 3103
Supervisory panel: Dr Nicholas Barrand (Birmingham); Dr Sergio Bacallado (Cambridge)

Industrial partners Marcus Engdahl. Technical Officer, European Space Agency (ESA)  Marcus.engdahl@esa.int

Timeline and what happens next

  1. Studentship applications closing date – Monday 13th March, 2017
  2. Notification of applicants selected for interview – Tuesday 14th March, 2017
  3. Interviews held – by Tuesday 28th March, 2017
  4. Notification of successful applicants – Friday 7th April, 2017
  5. If selected for Cohort 2: Commence by July 31st, 2017
  6. If selected for Cohort 3: DREAM induction – October 1st, 2017

Background: Overall, the DREAM Centre for Doctoral Training supports three cohorts of 10 students, who join our programme in 2015, 2016 and 2017 respectively. There are vacancies in our second and third cohort intakes of Dream studentships, with positions remaining open.

Our Dream students in post are now underway with their research projects, which are described here. All our positions are competitive and the student applications and interview stage allow us to identify and select the top applicants.

If you are interested in pursuing a PhD in the area of Big Data and environmental science and risk mitigation – we’d like to hear from you. We will have positions opening across our four universities – so contact the DREAM representative at one of the four universities to discuss – and send us your CV! We look forward to hearing from you.


FAQ

I am interested – what do I need to do?
The full selection of proposed doctoral projects is listed above, and is also available in other appropriate online channels, as well as on respective university noticeboards. You can make your application for up to two specific projects – in fact doing so may aid your chances. The DREAM student application form is provided above for this purpose.
I need more information
Please identify and contact one of the DREAM Management Board representatives who will be pleased to advise further.
Am I eligible to apply?
Further information is also available (e.g. on student eligibility etc.) on the NERC studentship FAQ. Please be sure to read this carefully before applying!
Residence requirements
To be eligible for a full award a student must have:

  • Settled status in the UK, meaning they have no restrictions on how long they can stay, and
  • Been ‘ordinarily resident’ in the UK for 3 years prior to the start of the studentship. This means they must have been normally residing in the UK (apart from temporary or occasional absences), and
  • Not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK or EU nationals).

For purposes of residence requirements the UK includes the United Kingdom and Islands (ie the Channel Islands and the Isle
of Man).

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