Research Students


A call to prospective students to apply for one of our fantastic PhD studentships NEW A new set of 2017 DREAM PhD studentships are now available – we look forward to hearing from you.
See how to make your application »


In October 2015, the first cohort of 10 Doctoral students got up and running in DREAM with their exciting and varied research topics. This cohort was also joined by a number of wider Doctoral studentships affiliated to DREAM. In October 2016, a further group of second cohort students joined DREAM, and now in 2017 we have added our third cohort of DREAM studentships. Finally, we have successfully interviewed and made offers for a further set of 7 studentships, making a total cohort of 37 researchers, plus affiliate positions – an extraordinary pool of research talent addressing the themes of DREAM. The research projects that DREAM supports are outlined below.

Also presented are some video clips showing was it is like being a DREAM student.

Cranfield University


Cranfield University-based projects  
For further details on these projects, contact Dr Stephen Hallett »
The impact of tree-related ground movement on water infrastructure
Modelling the impact on infrastructure resilience of local vegetation-influenced ground movement with multi-scale spatio-temporal environmental data The UK water network includes pipes of different materials, diameters and ages. These pipes are buried in different soils and have different external factors influencing their resilience. We know that ground movement breaks pipes, but due to the complexity of the environment and pipe network, predicting the number of bursts, and particularly where they are most likely to occur, remains very challenging.

Based on the location, height and canopy of the 28 million trees within our study area, you will calculate areas of ‘tree influence’ on soil moisture, and use these to predict highly local ground movements. It is expected that the incorporation of the new tree datasets into infrastructure-environmental models will enhance our ability to predict the location of burst pipes. Taking a large, historic 10 year, case study in the Anglian Water region, this will be the first time environmental-water infrastructure models based on soil, weather and tree variables will have been developed and tested on this scale.

You will test the relationships hypothesised using a range of statistical methods to assess the relative contributions of the infrastructure and environmental variables to each infrastructure failure. The changing impact of trees on local soil moisture under future climates will also be modelled.

Due to the age and variable integrity of the UK water network, this work will be of national importance in as it will help to focus the limited resources available for repair and replacement in the areas which will deliver the maximum benefit in reducing leakage and associated energy costs, collateral damage to nearby infrastructure, and minimising the potential impact of failures to both humans and the environment.
Matthew North
Student: Matthew North

First supervisor: Dr Timothy Farewell
Email: t.s.farewell@cranfield.ac.uk
Commenced: October 2015
Supervisory panel: Cranfield Dr Timothy Farewell; Dr Stephen Hallett   Cambridge Dr Mike Bithell   Industrial partner Anglian Water plc.; Bluesky Ltd.
Telephone: +44 (0)1234 752978

Coastal management and adaptation an integrated big data approach - improved risk based decision-making
Coastal management and adaptation an integrated big data approach - improved risk based decision-making - Norfolk coastline The coastline of Norfolk and Suffolk coastline has continually changed since the last ice-age. It is also an extensively studied coast and it is rich in data and information. However until now there has been no focussed effort to understand the range and depth of the data available and nor how to utilise the data to provide the evidence to enable better risk-based decisions to be made on the long-term future for a resilient coast.

This exciting opportunity will for the first time give unparalleled access to data bases in a range of risk management authorities. This together with other publicly available data will enable the candidate to undertake new interpretation of information, challenge or support current policies and provide evidence to enable better informed decisions to be taken in future. The non-academic partners to this project do not have preconceived ideas as to the out puts of this work only the recognition that they are currently weak in their understanding of both what actual relevant information is available and when combined and interpreted what new insight about the coast can be gained.

This work can potentially have a significant impact on local businesses and communities, so whilst an enquiring mind and technical expertise is essential the candidate will need to be proactive in communicating ideas and findings. They will also need to recognise that this is not just an academic exercise part of the success of the project will be measured on what legacy there is, can the risk management authorities continue to utilise the new techniques once the studentship has been completed. Access will be available to the technical experts along the coast, those who have insights into other issues, policy makers and senior management. In addition if real benefits of this work are identified and can be transferable to other local authorities and coastal risk managers then this work will be actively promoted through other local authorities, Environment Agency, etc.
Alexander Rumson
Student: Alexander Rumson

First supervisor: Dr Stephen Hallett
Commenced: October 2015
Supervisory panel: Cranfield Dr Stephen Hallett; Tim Brewer;   Industrial partner East Suffolk Council; Anna Harrison, British Geological Survey

Managing complexity: big data underpinning environmental and economic sustainability in the UK water industry
Managing complexity: big data underpinning environmental and economic sustainability in the UK water industry This studentship will use a case study approach to develop a new generation of data-oriented informatic tools for the management of the complex and multiple data underpinning total expenditure decision-making in the water utility sector. The proposed programme of research will focus on a comparative evaluation of existing multi-stakeholder tools for asset management planning and will develop a series of analytical tools, exploring contrasting technical and software approaches. The research will lead to an understanding of the range and applicability of multivariate analytic tools, and will ultimately assess how these complex outputs may be visualised such that customer-driven outcomes are derived and management decision-making is supported. Data will be drawn from case studies drawing on the Atkins UK and EU water industry networks.

Specific case studies for this research will derive from the UK Chemical Investigations Programme (CIP) (£35m) research innovation programme (2010-2013), managed by Atkins and informing UK and EU chemical regulation under the Water Framework Directive (https://www.ukwir.org/site/web/news/news-items/ukwir-chemicals-investigation-programme). Working with Defra, UK Environment Agency, UK Water Industry and Ofwat, and in response to emergent legislation on surface water quality, CIP sought to gain better understanding of the occurrence, behaviour and management of trace contaminants in wastewater treatment process and in effluents. CIP has generated a vast and varied body of data that now paves the way in this study for the development of tools for rational prioritisation of future actions, supporting a transparent and informed discussion of the available options required to manage trace substances in the water environment. Further to this, Atkins are also now supporting the UKWIR ‘follow-on’ programme (£120m) of UK innovation, embracing environmental challenges, technological development and economic decision-making. Additional case studies can draw from this programme, providing a basis for further integrated analytical tools. The outline for multivariate analysis tools linked to visualisation of outputs in this arena will be used to inform outcomes and to explore opportunities in the water sector.

A unique aspect of this project is that it will seek to inform the UK water industry as to the options and opportunities for meeting EU and UK environmental drivers and, due to its economic implications, it will also inform the introduction of competition into the UK water market. Therefore the proposed case studies approach should prove of significant importance to the UK on both an environmental and economic level.
Juan Manuel Ponce Romero
Student: Juan Manuel Ponce Romero

First supervisor: Dr Stephen Hallett

Email: s.hallett@cranfield.ac.uk
Telephone: +44 (0) 1234 752750
Commenced: October 2015
Supervisory panel: Cranfield Dr Stephen Hallett; Dr Simon Jude   Industrial partner Atkins plc..

Multiscale prediction of groundwater response to extreme events
Multiscale prediction of groundwater response to extreme events The day-to-day dynamics of groundwater behaviour are driven by local weather and recharge patterns, but previous research has identified that there are also significant temporal relationships large scale ocean-atmosphere conditions. This project will seek to expand on this earlier work by combining advanced statistical methods, extensive spatiotemporal (including climatological, meteorological, hydrogeological) datasets and groundwater modelling to further analyse these relationships so as to be able to better predict and manage groundwater level response to extreme events. The research will focus on a large dataset of long term groundwater levels records from boreholes across the UK (and more broadly in Europe where available) to understand the differences in sensitivity between different aquifers or climatological areas. The student is supported by an experienced supervisory team from Cranfield and Birmingham universities and the British Geological Survey and will be provided by extensive training within the DREAM programme’s Advanced Technical Skills and Transferable Skills and Leadership training.
William Rust
Student: William Rust

First supervisor: Professor Ian Holman
Email: i.holman@cranfield.ac.uk
Telephone: +44 (0) 1234 758277
Commenced: October 2015
Supervisory panel: Cranfield Professor Ian Holman; Dr Ron Corstanje   Birmingham Dr Mark Cuthbert   Industrial partner British Geological Survey

Near real-time correction of flood forecasting using high resolution satellite data for multi-hazard risk assessment in lowland tropical regions
Petrochemical infrastructure inundated by flooding, Mexico Flood modelling and forecasting are essential tools to inform infrastructure and emergency planning. Accurate forecasts, though, are difficult to achieve, even in developed countries with decades of experience and detailed topographical and hydrological datasets for calibration and validation, as demonstrated by the recent Cumbria floods. Forecasting is even more challenging in large tropical regions, which have limited data availability (e.g. few river gauging stations), and modest meteorological forecasting capabilities. More worryingly, their large human populations, economically-important industries (e.g. oil/gas, agriculture), and ecologically-important habitats mean that flooding is connected to multiple other significant risks.

This PhD project will attempt to overcome some of these challenges by (1) using new near real-time, high resolution satellite datasets to improve the medium- and short-term flood risk assessment generated by probabilistic ensemble flood forecasting for data-poor tropical regions, and (2) applying the flood model to the assessment of flood-induced pollution risk. The case study for the project will be the Mexican State of Tabasco, which occupies a large, low-lying, topographically-complex area that experiences flooding from several large rivers (e.g. Grijalva-Usumacinta systems, 1,911 km long network, 128,390 km2 catchment area) which are affected by weather systems from both the Pacific Ocean and Caribbean Sea. The State is home to a large, economically-important on- and off-shore oil industry, which is both impacted by the flooding and a source of significant pollution risk during floods. Consequently, accurate flood forecasts are needed to advise the population, protect or relocate sensitive oil extraction and refining infrastructure, and to assess the risk of pollutant mobilisation (i.e. oil and associated chemicals) which could significantly impact water quality, agriculture or sensitive ecological habitats.

The student will work closely with academics at the Universidad Juaréz Autónoma de Tabasco (UJAT), who have offered additional support for the project to allow the student to spend a significant amount of time in Mexico (2-3 months per year) and to access new high-resolution, 1-day return period satellite data. The project will have a direct and immediate impact on flood and pollution risk management in Tabasco, as the PhD outputs will feed into UJAT’s development of an operational water risk management system for the State.
Charles Mazivanhanga
Student: Charles Mazivanhanga

First supervisor: Dr Bob Grabowski
Email: r.c.grabowski@cranfield.ac.uk
Telephone: +44 (0) 1234 758360
Commenced: October 2016
Supervisory panel: Cranfield Dr Bob Grabowski; Tim Brewer   Industrial partner University of Tabasco, Mexico

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.

Student: Martina Della Casa

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

Reducing water leakage through the development of high resolution soil maps

The problem:
Vast amounts of clean water is lost from the water supply network each year. Ageing pipes often fail as a result of soil corrosivity, or the seasonal shrink-swell cycle of clay soils. Soil spatial distribution is complex and existing soil maps[1], while useful, do not provide sufficient detail to identify vulnerable water network segments for upgrade.

This research will address these problem through delivering three components:

  1. Creation of new, detailed soil hazard maps, predicting soil corrosivity and shrink-swell potential at 50m resolution.
  2. Development of enhanced pipe failure models which predict where and when pipes will fail on the basis of soil, weather and infrastructure parameters.
  3. Advising Anglian Water on which pipes to upgrade to reduce leakage, energy use and customer interruptions.

Through the research, developing and using high resolution predictive soil maps[1],[3] and burst models, water companies will be able to better identify, and upgrade, vulnerable parts of their networks. The many resulting benefits from your work will include a reduction in leakage, reduction in energy use and reduction in interruptions to customer supplies.

Our climate is changing[2]. Hotter drier summers cause issues for water supply, and the chaotic weather patterns cause havoc with traditional infrastructure modelling. The old approach of comparing infrastructure performance with previous months or years no longer is fit for purpose. THis project will be closely aligned to industry, seeking to better our understanding of the dynamic interactions between soil, weather and infrastructure. Through the research, we will be able to improve our ability to benchmark and improve network performance, developing new digital soil mapping techniques to enhance our understanding of the soil. The outputs will be integrated into predictive burst models, and used to provide guidance to Anglian Water on where it can best target it’s financial investments, so they can reduce leakage to a negligible level.

[1] LandIS – the Land Information System (www.landis.org.uk)
[2] Bluesky National Tree Map (www.blueskymapshop.com/products/national-tree-map)
[3] Met Office 3 hourly forecast data: (www.metoffice.gov.uk/datapoint/product/uk-3hourly-site-specific-forecast)


Student: Giles Mercer
For further details: Please contact Dr. Timothy Farewell (Cranfield), Senior Research Fellow in Geospatial Informatics

Email: t.s.farewell@cranfield.ac.uk
Telephone: +44 (0) 1234 752978

Industrial partner: Anglian Water

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.

Student: Karolina Golicz

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.

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

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.
Student: Matthew Webb

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 close collaboration with Natural England.

DIGITAL METROPOLiS - Digital METRics OPtimising Outcomes for infrastructure Life-cycle Integration Strategies
The future is already here — it’s just not very integrated…

Currently around 50% of the world’s population lives in cities, this will grow to 75% by 2050. The OECD estimate it will cost $45 trillion USD between now and 2050 to upgrade infrastructure in our towns and cities across the world whilst also to managing the impact of climate change.

Here in the UK, the Government for example is targeting a 33% cost reduction across the whole life of assets by 2025, what’s more they are demanding 50% reduction in time and 50% reduction in greenhouse gas emissions.

The infrastructures of our cities; buildings, roads, power, water, internet are increasingly interconnected and Digital Engineering will transform the way in which construction projects are conceived, planned, executed – and the way in which infrastructure assets are created, operated and used by citizens. 3D object modelling, laser scanning, drones, augmented reality and the internet of things are just some of the technological innovations which are already transforming infrastructure systems.

Working with Atkins, a global cross-sector infrastructure design consultancy, this project will understand how enhanced social, economic and environmental outcomes can be achieved by the adoption of Digital Transformative Technologies and protocols. Research methods will be developed to determine where city infrastructure system integration is appropriate, how metrics and measures of benefits may be established and consequently indicate how optimal, integrated infrastructure systems management could be developed. Appropriate integrated cross-sector case studies will be developed to demonstrate the findings and provide real world context.

The project will include opportunities for secondment to work alongside Atkins staff to gain a valuable first hand experience of working in this sector. The wider DREAM CDT offers an exciting range of training and support to develop both technical and personal skills.
Student: Avgousta Stanitsa

For further details: Please contact Dr Stephen Hallett DREAM CDT Director

Email: s.hallett@cranfield.ac.uk
Telephone: +44 (0) +44 1234 750111 x2750

Supervisory panel: Cranfield Dr Stephen Hallett; Dr Simon Jude   Industrial partner: Dr Arthur Thornton, Associate Director: Infrastructure, Atkins Global plc.
Atkins, Epsom office, KT18 5BW
Tel: +44 (0)1226370233 | Email: arthur.thornton@atkinsglobal.com

Predicting the locations of burst water mains using weather forecast

The problem:
Water pipes often fail as a result of weather conditions, such as cold weather events, or rapid changes in soil moisture deficit. Because water companies do not know where or when to expect burst pipes, they do not respond as fast as they would like. This means that valuable water is lost, the energy used to treat that water is wasted, and people are without water, sometimes for sustained periods.

This research will seek to predict where and when pipes will fail, enabling water companies to be on hand to respond more quickly to burst water mains. There are three main stages to this PhD. To:

  1. Develop and test back-looking high resolution predictive burst models based on soil[1], weather and infrastructure parameters.
  2. Develop and test forward-looking predictive models of burst locations for the coming week, using available forecast data.
  3. Work with Anglian Water to build an early warning system which identify areas of the network to prioritise for maintenance in the coming week

The work will result in a new data tool which will enable Anglian Water to predict where and when its pipes should burst given the predicted weather conditions. This will enable the water utility to more quickly respond to bursts as they will have a better understanding of where the bursts are likely to occur. The research is closely aligned to industry, and seeks to better our understanding of the dynamic interactions between soil, weather and infrastructure.

The PhD will involve developing big data tools which enable cleaning, processing and use of the vast datasets ranging from 15 minute interval pipe pressure management data, 3 hourly weather forecast data[3] and more static datasets which describe the soil1, vegetation[2] and infrastructure parameters. Unlike some PhD projects, this project seeks to solve a real world problem. As well as being academically stimulating, the project has industrial backing, with involvement from front-line infrastructure operators. The research seeks to help improve their service through the use of the research results.

[1] LandIS – the Land Information System (www.landis.org.uk)
[2] Bluesky National Tree Map (www.blueskymapshop.com/products/national-tree-map)
[3] Met Office 3 hourly forecast data: (www.metoffice.gov.uk/datapoint/product/uk-3hourly-site-specific-forecast)
Student: Neal Barton

For further details: Please contact Dr. Timothy Farewell (Cranfield), Senior Research Fellow in Geospatial Informatics

Email: t.s.farewell@cranfield.ac.uk
Telephone: +44 (0) 1234 752978

Industrial partner: Anglian Water

NextGenPhenomics: Building a systems-level cloud-based platform, integrating Next Generation Sequencing and advanced real-time phenotypic quantification for linking genotype to phenotype in plants
Background:
The falling costs and the extremely high yield of genomic DNA sequencing data from next generation sequencing (NGS) technologies means that it is now routine to produce more than one billion sequencing reads within a few days[1]. This allows us to describe nearly all the sequence differences between hundreds of different plant lines[2,3]. But, to maximise the benefit from these rapid advances in NGS, we also need “next-generation phenotyping” to link genotypes and phenotypes—this allows us to understand which DNA differences cause a plant to look or respond differently. These differences can then be used by plant breeders to select genetic combinations that perform better in particular environments[4].

Cranfield University and AgriEPI Centre have recently acquired a state-of-the-art phenotypic platform installed within a purpose-built 300 m2 glasshouse facility as part of a £5.5m investment. This unique LemnaTec® multi-sensor platform moves in three dimensions within the partially environmentally controlled glasshouse, while the plants remain static in containers of up to 1 m3. It is designed to precisely monitor the growth and physiology of crops under a range of soil conditions and rootzone stresses such as salinity, drought and compaction using RGB, hyperspectral, fluorescence and thermal cameras and a 3D laser scanner.

Objectives:
In ongoing projects at Cranfield, the response of a population of tomato genotypes to various rootzone stresses (e.g. drought, compaction, salinity, pH) will be assessed using the Lemnatec platform, and the genotypes of these plants will be defined NGS.

The aim of this PhD is to develop a cloud-based platform, integrating NGS and phenotypic measures acquired via the LemnaTec platform. Due to huge size and heterogeneous nature of phenotypic and genotypic data being integrated, the developed platform will be coupled with a Big Data-compatible database backend (e.g. Hadoop, NoSQL). The database will host a variety of short and long read sequencing genomic (and possibly transcriptomic) data. The platform will be coupled with an interactive Web-based UI that allow the integration of phenotypic and genomic datasets by providing several data analysis pipelines for automating the variant calling and SNP identification as well crawling genomic and proteomic annotation from public databases. A series of mathematical models, including machine learning and pattern recognition models, will be developed to allow prediction of genotypic impacts on plant phenotypes.

References:
[1]Metzker, M.L., Sequencing technologies – the next generation. Nat Rev Genet, 2010. 11(1): p. 31-46.
[2]Koboldt, D.C., et al., The next-generation sequencing revolution and its impact on genomics. Cell, 2013. 155(1): p. 27-38.
[3]Morozova, O. and M.A. Marra, Applications of next-generation sequencing technologies in functional genomics. Genomics, 2008. 92(5): p. 255-64.
[4]Hennekam, R.C. and L.G. Biesecker, Next-generation sequencing demands next-generation phenotyping. Hum Mutat, 2012. 33(5): p. 884-6.
Student: Ewelina Sowka

For further details: Please contact Dr Fady Mohareb (Cranfield), Senior Lecturer in Bioinformatics
Email: f.mohareb@cranfield.ac.uk

Cranfield University Supervisors: Dr Fady Mohareb, Prof. Andrew Thompson
Industrial partner: AgriEpi Centre

FoodML: Development of a food quality and safety risk management system, using cloud computing, big data and data science
Background:

Currently, food quality and safety controls relies heavily on regulatory inspection and sampling regimes. Such approaches are often based on conventional chemical and microbiological analysis, making the ultimate goal of 100% real-time inspection technically, financially and logistically impossible.

Over the past decade, rapid non-invasive techniques (e.g. vibrational spectroscopy, hyperspectral / Multispectral imagining) started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques.

Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine learning algorithms before the results can be interpreted. Although these platform has been showing great potentials to accurately and quantitatively assess freshness profiles (Panagou, Mohareb et al. 2011) (Mohareb, Iriondo et al. 2015) and safety parameters as well as adulteration (Ropodi, Panagou et al. 2016), their dependence on advanced data mining and statistical algorithms made was the main challenge facing their practical implementation across the food production and supply chain.

In order to overcome these challenges, we have developed sorfML (http://elvis.misc.cranfield.ac.uk/SORF), a Web platform prototype compatible with outputs from 5 instrumental platforms (See Figure) which provides means for interactive data visualisation, multivariate analysis (Principal component analysis and hierarchical clustering), as well as the ability to use stored datasets to develop predictive models to estimate food quality. Currently, the platform provides users with means to upload their experimental datasets to the server, thanks to the truly generic MongoDB NoSQL database backend, and to develop classification and regression models to estimate quality parameters.

Objectives:
The aim of this PhD is to expand the existing sorfML platform into a cloud-enabled framework that supports real-time monitoring of food products throughout the production chain. In order to achieve this, a series of advanced portable sensory devices will be deployed to examine their suitability as “Connected devices” in predicting quality and safety indices for various food perishable food products. A series of machine learning and pattern recognition models will be developed and integrated within the cloud system. This includes Ordinary Least Squares, Stepwise Linear classification and regression, Principal Component regression, Partial Least Squares discriminant analysis, support vector machine, Random forests and k-Nearest Neighbours.

References:
Mohareb, F., M. Iriondo, A. I. Doulgeraki, A. Van Hoek, H. Aarts, M. Cauchi and G.-J. E. Nychas (2015). “Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling.” Food Control 57: 152-160.
Panagou, E. Z., F. R. Mohareb, A. A. Argyri, C. M. Bessant and G. J. Nychas (2011). “A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints.” Food Microbiol 28(4): 782-790.
Ropodi, A. I., E. Z. Panagou and G. J. E. Nychas (2016). “Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines.” Trends in Food Science & Technology 50: 11-25.
Student: Emma Sims

For further details: Please contact Dr Fady Mohareb (Cranfield), Senior Lecturer in Bioinformatics
Email: f.mohareb@cranfield.ac.uk

Cranfield University Supervisors: Dr Fady Mohareb, Prof. Andrew Thompson
Industrial partner: Centre for Crop Health and Protection (CHAP)

Newcastle University


Newcastle University-based projects  
For further details on these projects, contact Dr Stuart Barr »
Global infrastructure flood risk analysis using big data
Global infrastructure flood risk analysis using big data Infrastructure systems (energy, transport, water, waste and telecoms) globally face serious challenges. Analysis in the UK and elsewhere identifies significant vulnerabilities, capacity limitations and assets nearing the end of their useful life. Against this backdrop, policy makers internationally have recognised the urgent need to de-carbonise infrastructure, to respond to changes in demographic, social and life style preferences, and to build resilience to intensifying impacts of climate change.

This PhD will draw on advances in (i) methods for broad scale infrastructure risk analysis, (ii) readily available datasets describing global climate and associated hazards, global exposure, and increasingly information on the location of key infrastructure networks, and, (iii) ‘big data’ processing and cloud computing techniques, to enable the first global infrastructure risk analysis.

The research will develop an integrated model that uses data from global mapping sources such as Google, Open Streetmap; i-COOL global marine networks (port flooding); CAA (airport flooding); global flood hazard maps (WRI: floods.wri.org) and climate model outputs (climateprediction.net); population location (Global Rural Urban Mapping of Project) to look at future risks.

The project will develop an integrated assessment model of global transport networks, where the importance of major infrastructure network components are assessed based upon population served,
information on route type (e.g. main, secondary road etc.), other published information (e.g. route frequency for airlines) and so on. This information, integrated with hazard extents, will provide a unique global risk assessment.

The size of the spatial datasets necessitates a cloud or distributed computing approach to handle and process the data. Web-enabled tools will be developed and the integrated framework for managing the workflow of these web-based tools will be designed with extensibility in mind to enable other researchers to augment the model as new data and capabilities become available.
Feargus McClean
Student: Feargus McClean

First supervisor: Professor Richard Dawson

Email: richard.dawson@newcastle.ac.uk
Telephone: +44 (0) 191 208 6618
Commenced: October 2015
Supervisory panel: Newcastle Professor Richard Dawson; Dr Sarah Dunn; Dr Hiro Yamazaki; Dr Sean Wilkinson

Modelling flooding from multiple sources using coupled models and multi-scale high resolution datasets
Modelling flooding from multiple sources using coupled models and multi-scale high resolution datasets The project will address a current national issue of evaluating flood risk from multiple sources (fluvial, pluvial, and groundwater), something that has become evident in the extensive flooding over the past 10 years (e.g. summer flooding in 2007, and winter flooding in 2013/14). Existing computer modelling techniques are designed to handle each type of flooding separately. This study will integrate existing state-of-the-art models to provide a novel modelling capability, which will be used with new data on high-intensity rainfall patterns. Following model integration and testing for a range of case study sites across the UK, the integrated model will be used with rainfall data representing current and possible future climates to assess the enhanced flood risk arising from multiple sources, providing an improved basis for flood management in the UK and a methodology that can be applied internationally.

Depending on the background of the student, training will be provided through formal courses in hydrogeology, 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.
Ben Smith
Student: Ben Smith

First supervisor: Dr Geoffrey Parkin

Email: geoff.parkin@ncl.ac.uk
Telephone: +44 (0) 191 208 6618
Commenced: October 2015
Supervisory panel: Newcastle Dr Geoffrey Parkin; Professor Hayley Fowler

Real-time estimation of lake volume and river discharge from satellite altimetry and remotely sensed imagery in ungauged basins
Real-time estimation of lake volume and river discharge from satellite altimetry and remotely sensed imagery in ungauged basins Remote sensing provides the mechanism for forewarning of risks of potential loss of life due to water failure and flooding through monitoring of reservoir and lake levels and river discharge. Further, many river catchments are managed with dams constructed for hydroelectric power, fisheries and water resources and these dams often have a detrimental effect on livelihoods, particularly downstream while river discharge across major catchments suffer from either lack of gauge data or data unavailability. With precipitation data lacking in many parts of the world, information concerning water failure or flood events is often not communicated downstream with potentially catastrophic consequences.

Near real-time quantification of lake/reservoir levels and volumes and river stage heights and discharge can be recovered from satellite altimetry, an estimation of the lake area or river width from near real-time satellite imagery and some mechanism to develop a stage-discharge relationship perhaps based on a single gauge data or hydrological modelling. This project will develop a near-real time capability for the latest delay-doppler type of altimeter (carried onboard Cryosat-2 and the soon to be launched Sentinel3 satellites) and optical and/or Synthetic Aperture Radar imagery for river and reservoir/lack extent. The river mask will be used to both constrain the satellite altimetry to the inland water target but also supply river width ad lake extent for inferences of variations in discharge and lake volume.

Such a capability will reduce the risk associated with water failure and floods providing an early warning with time lapse of less than 24 hours limited by the time that the quick-look satellite altimetric waveforms are made available to the user. The successful applicant will receive advanced training, depending on background, in topics such as Big Data, Cloud and Distributed Computing, Software Reliability, Modelling of Floods, Real Time Flood Forecasting and Warning Systems, Signal Analysis, Time series Analysis etc.
Miles Clement
Student: Miles Clement

First supervisor: Professor Philip Moore

Email: philip.moore@ncl.ac.uk
Telephone: +44 (0) 191 208 5704
Commenced: October 2015
Supervisory panel: Newcastle Professor Philip Moore; Professor Chris Kilsby; Professor Philippa Berry

Multi-objective spatial risk and sustainability optimisation tools using cloud computing
Sustainability Optimisation Tools Spatial risk assessment cannot be considered in isolation from other factors such as the development of long term sustainable plans for land use development and the implementation of planning decisions that mitigate adverse climate change impacts such as increased heat. To date however, the ability to develop spatial, multi-objective risk and sustainability planning tools has been limited by intractable computational run-times. Cloud computing now offers the potential to overcome this limitation, facilitating the development of spatial optimisation risk and sustainability planning tools that allow a large number of risk and sustainability objectives to be considered in the production of optimised spatial development plans.
In this PhD, cloud computing will be employed to provide the next generation of multi-objective, spatial risk and sustainability development plans for the UK. Using nationally available data-sets on climate related hazards, such as probabilistic predictions of heat, pluvial, fluvial and storm surge related flooding, along with future predictions of population demographics, the PhD will investigate how temporal spatial plans can be developed that minimise exposure to future spatial risk whilst maximising key local, regional and national sustainability objectives (e.g., minimising overcrowding, reducing urban sprawl, maximising access to low emission public transport etc.).
During the PhD, cloud computing training will be provided via a number of the Newcastle EPSRC CDT Big Data & Cloud Computing modules.
Grant Tregonning
Student: Grant Tregonning

First supervisor: Dr Stuart Barr

Email: stuart.barr@ncl.ac.uk
Telephone: +44 (0) 191 208 6449
Commenced: October 2016
Supervisory panel: Dr Stuart Barr (Newcastle); Professor Richard Dawson (Newcastle); Dr Raj Ranjan (Newcastle)

Improving mass change estimates from gravimetry with applications to basin scale polar ice and terrestrial water change
The Gravity Recovery and Circulation Explorer (GRACE) mission, a tandem satellite pair, was launched in 2002 and has provided unprecedented insight into mass change including polar ice mass, glaciation, hydrology and earthquakes. The novel aspect is the inter-satellite microwave ranging device that is accurate to a few microns and is highly sensitive to the differential pull from gravity on the two satellites. Solutions for gravity field snapshots are made every 10 days to a month with the differences revealing mass change associated with surface processes once longer term trends such as Glacial Isostatic Adjustment (GIA) are allowed for. Of the solution approaches the mass concentration (mascon) method has been established as superior to spherical harmonics. At Newcastle 2 degree mascons over successive 10 day periods form the basis of time series of mass change in our current studies. The mascons are constrained geographically over Antarctica, Greenland, land and the oceans to enable a solution to be obtained for over 10,000 masons globally. The usage of 2 degree masons is a limiting factor and needs to be refined for basin-scale change detection, particularly in delineating the basin boundaries.

The GRACE mission is near the end of its operational lifetime but is still providing data. The uniqueness and scientific value of the mission has led to a GRACE Follow-on (FO) mission scheduled for launch in December 2017/January 2018. The GRACE FO mission has the same inter-satellite K band microwave device for providing range-rate measurements but will also carry a laser interferometer on each satellite to provide range and directional data. Although experimental and not continuously operational, when available the laser data will be an enhancement over the microwave data.
Student: Jerome Richmond

First supervisor: Professor Philip Moore (Newcastle):
Tel: +44 (0) 191 208 5040
Email: philip.moore@ncl.ac.uk

Supervisory panel: Professor Philip Moore (Newcastle); Dr Ciprian Spatar (Newcastle)

University of Cambridge


University of Cambridge-based projects  
For further details on these projects, contact Dr Mike Bithell »
Panda Alert: understanding the significance of adverse health event reports using distributed semantic models
Panda Alert: understanding the significance of adverse health event reports using distributed semantic models With concerns about the rapid spread of new and re-emerging diseases such as Ebola and A(H1N1) influenza there has been increasing attention on human language technologies which can complement traditional information sources by trawling through news and social media data on a Web-scale to find ‘the needle in the haystack’ that indicates a first adverse health event report.

Potential PhD students with a strong background in Computer Science, Computational Linguistics or Artificial Intelligence are encouraged 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 is expected to start in the academic year 2015/16. The student will work within an interdisciplinary team of researchers from Computer Science, Computational Linguistics and Public Health in the Panda Alert project which investigates novel methods for health risk alerting using natural language processing, machine learning and distributed semantic representations.

Motivated by the challenges outlined above the student will examine natural language understanding technology within a high performance computing environment. The exact scope of the project is open to discussion and will be shaped by the student’s strengths but we anticipate that the successful candidate will develop a novel approach for early health risk alerting that combines the state of the art in natural language processing with aberration detection to achieve high throughput real-time alerting of novel health threats.

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 the Language Technology Laboratory, a friendly research-led team that conducts weekly meetings, publishes in top conferences and journals and with extensive inter-disciplinary collaborations. This is a unique opportunity to work at the cutting edge of computational linguistics.
Milan Gritta
Student: Milan Gritta

First supervisor: Dr Nigel Collier

Email: nhc30@cam.ac.uk
Telephone: +44 (0)1223 7 67356
Commenced: October 2015
Supervisory panel: Cambridge Dr Nigel Collier; Dr Anna Korhonen   Industrial partner Joint Research Centre of the European Commission (JRC EC)

Cold Fish
Cold Fish The aim of this project is the bioinformatics analyses of the genes of Antarctic fish to assess the risk of the potential for climate change induced extinction of high latitude fish species, and the impact on high latitude Southern Ocean fisheries. Antarctic fish have evolved in isolation for millions of years in a very stable, near-freezing environment and have developed a number of well-documented adaptations to cope with life in the cold (e.g. antifreeze glycoproteins, large muscle fibres and lack of haemoglobin).

Unlike their temperate counterparts, these fish are remarkably sensitive to even small changes in temperature: they are highly stressed by just a 3°C rise in their environment and die at temperatures 5°C above normal physiological ranges. There is strong evidence that this may be caused by the instability of the proteins at around 0°C and that protein adaptation to cold temperatures is only marginally successful and that the Antarctic marine fauna are only just “clinging on” to life in their environment (see for instance http://www.sciencemag.org/content/347/6226/1259038.full.pdf). However, to date, there has been no comprehensive analysis of the proteome of these species to identify if this protein instability is restricted to certain key proteins or is more widespread.

Given that the waters around the Antarctic Peninsula (a key fishing ground for Antarctic fish) is warming at one of the most rapid rates on the planet and that the Antarctic fisheries are the only large-scale exploitation going on in Antarctica, there is a real risk of extinction for key Antarctic fish species and collapse of the Southern Ocean fisheries industry. This project will be based between the University of Cambridge, Department of Computing and the British Antarctic Survey. The aim is to examine the transcriptomes of 8 Antarctic fish species and mine this extensive dataset to identify how extensive cold-adapted changes are in Antarctic fish and therefore predict future vulnerabilities.

The student will receive comprehensive training in bioinformatics skills, namely transcriptome assembly and annotation, alignments and phylogenetics analyses to detect regions within genes under selection and characterisation of pathways or subnetworks under selection pressure, network regression analyses will be employed to identify markers and trajectories of adaptation and homology modelling will be used to predict structural constraints and performance under different environmental scenarios. They will also benefit from extensive complementary skills training provided by the DREAM DTP, Cambridge University and the British Antarctic Survey.
Pablo Spivaskovsky-Gonzalez
Student: Pablo Spivaskovsky-Gonzalez

First supervisor: Dr Pietro, Lio’

Email: Pietro.Lio@cl.cam.ac.uk
Telephone: +44 (0)1223-763604
Commenced: October 2015
Supervisory panel: Cambridge Dr Pietro, Lio’   Industrial partner Dr Melody Clarke, British Antarctic Survey (BAS), mscl@bas.ac.uk

Temporal and spatial patterns of shoreline change and exposure of coastal communities and ecosystems to future flood risk
Shoreline Change The project will involve the synthesis of a large number of environmental datasets, from historical archives to near real-time oceanographic data and newly available satellite imagery, to establish the temporal and spatial patterning of shoreline change on the 45 km-long barrier coastline of North Norfolk, eastern England; the development of shoreline response models, and their coupling to storm surge models, to explore near-future shoreline positions; and the integration of environmental hazard modelling with indicators of socio-economic vulnerability to define new assessments of near-future risks to coastal communities and infrastructure.

Visualisation techniques will be used as part of the communication of project results to a wide range of coastal managers and decision makers. The successful candidate will need to be able to acquire, archive and analyse large environmental datasets and engage on a personal level with a wide range of coastal stakeholders. Some field visits will be required. Training will be provided in GIS, image analysis (including Copernicus Sentinel-2 imagery), environmental modelling and visualisation techniques, as well as presentation and thesis writing skills. The student will join active postgraduate clusters in the Cambridge Coastal Research Unit and at Cranfield University, as well as interacting with wider DREAM research community.
James Pollard
Student: James Pollard

First supervisor: Dr Thomas Spencer

Email: tom.spencer@geog.cam.ac.uk
Telephone: +44 (0)1223 7 67356
Commenced: October 2016
Supervisory panel: Dr Thomas Spencer (Cambridge); Dr Simon Jude (Cranfield); Dr Stephen Hallett (Cranfield)  Industrial partner Dr Geoff Smith, Technical Director. Specto Natura Ltd.

University of Birmingham


University of Birmingham-based projects  
For further details on these projects, contact Dr Emmanouil Tranos »
Geochemical processes affecting the mobility of colloids and associated radionuclides: long-term evolution of engineered repository barriers at geological disposal facilities
Geochemical processes affecting the mobility of colloids and associated radionuclides: long-term evolution of engineered repository barriers at geological disposal facilities Geological Disposal Facilities (GDFs) are, at present, the preferred option for the safe disposal of high and intermediate level waste. The internationally accepted conceptual model that has been firmly established for the last 30 years is based upon the ‘multi-barrier system’, whereby a series of engineered and natural barriers act together to isolate the wastes and contain the associated radionuclides. One of the disposal concepts under consideration is the KBS-3V disposal concept, a multi-barrier system consisting of a metal container, copper overpack, buffer/backfill and a high strength natural rock. A key part of this concept is the use of bentonite clay as the engineered barrier, it swells in contact with water to provide a mechanical buffer to protect the metal container and to mop up any radionuclides that may might leak from the container.

Water seeping from the natural geological environment produces bentonite hydration, swelling and the formation of a clay gel that can penetrate into pores and fine fractures of the rock. Chemical or physical erosion processes on the clay surface gel may generate mobile nano-sized colloidal particles which are potential carriers for radionuclide contaminants. Recent studies have shown that the in-situ migration of actinides (i.e. Am and Pu) were strongly facilitated by bentonite colloids. For this reason, it is important to study colloid generation mechanisms, to establish their role on radionuclide transport in the environment and to evaluate the associated risks.

The safety case for the implementation of a GDF requires the consideration of processes evolving over a long timescale, these necessitate the development of sound predictive modelling tools that are based on rigorous short term experiments and sensible extrapolation over the lifetime of a GDF. The technical approach that will be adopted in this study therefore uses a variety of methods (laboratory experimentation and modelling) to build evidence for a greater understanding and use of bentonite buffers that will underpin the required safety case for a GDF. Amongst the questions we will attempt to address are: (1) How is radionuclide uptake and mobility over rock affected by clay colloid particle size, morphology and surface charge? (2) What clay colloid particles are released by bentonite materials, what is their radionuclide uptake and mobility? (3) Can colloid mobility be successfully modelled under different hydrogeochemical conditions at the level of an intact natural fracture core sample in the laboratory? (4) Can this then be represented appropriately in field-scale numerical models?
Lauren Dackombe
Student: Lauren Dackombe

First supervisor: Dr Stephanie Handley-Sidhu

Email: s.handley-sidhu@bham.ac.uk
Telephone: +44 (0)121 414 6172
Commenced: October 2015
Supervisory panel: Birmingham Dr Stephanie Handley-Sidhu; Dr Alan Herbert; Dr Joe Hriljac   Industrial Partner Nuclear Decommissioning Authority

Risk of landscape failure examined through big data analysis; implications of Oil Sand mine closure planning
Landscape Failure The reconstruction of the Alberta oil sands represents one of the largest restoration challenges on the planet. Such constructed landscapes must be designed to develop rapidly over time, producing expansive productive forests that maximize the economic potential of the landscape and maintain high water fluxes to flush ecologically harmful salts. At the same time, the landscape design must limit the risk of failure, forming resilient environments which follow planned trajectories within the dry sub-humid climate of the Western Boreal. This research project integrates sound mathematical and statistical analysis of big data to identify the sources of such risk, their drivers and impacts within these complex boreal systems.

A system dynamics model informed from current process based understanding will be developed representing hydrological stores and transfers within the Western Boreal Plain. These landscapes have formed the focus of close to two decades of research by the project partners; legacy data which is being compiled into a readily available data rich achieve (measurements from ~15000 wells in addition to a range of supplementary hydrological, meteorological and ecological data). The models will be optimised and evaluated against this hydrological data. The modelling approach will be developed within STELLA, an intuitive icon-based graphical interface that enables detailed exploration and development of system dynamics. It will offer the opportunity to develop multi-level, hierarchical model structures that can serve as building blocks for large complex systems. The top down approach will enable key leverage points to be identified and the analysis of different system organisations and structures. The modelling framework will be applied to explore how the climate cycle, a superposition of varying climate signals of different intensities and phases, provides the overarching driver of the ecohydrology behaviour of these landscape. Further, how this climate cycle cascades through landscape storage units that vary in configuration, extent and scale of connectivity. It will determine how such complex interactions trigger periods of water scarcity over varying spatial and temporal scales that place individual ecosystem at risk of failure. The project will identify how such collapses and the resultant shift in hydrological function result in a loss of system resilience that may cascade through the landscape leading to its potential large scale failure.

The research will also involve integration within the Water Science group and Birmingham Institute of Forest Research (BIFoR), joining the core team of researchers within the Hydrology Ecology and Disturbance (HEAD3) project funded in partnership by industrial partners Syncrude and Canadian Natural Resource Ltd. This consortium composes of the University of Birmingham, University of Alberta, McMaster University and University of Waterloo.
Samantha Probert
Student: Samantha Probert

First supervisor:Dr Nick Kettridge

Email: n.kettridge@bham.ac.uk
Telephone: +44 (0)121 414 3575
Commenced: October 2016
Supervisory panel: Dr Nick Kettridge (Birmingham); Dr Geoffrey Parkin (Newcastle); Professor David Hannah (Birmingham); Industrial partners Dallas Heisler. Hydrogeological Research Engineer, Syncrude  

(Tele)commuting, cities and weather conditions
Telecommuting This project aims to explore the relationship between (tele)commuting and weather. Researchers have spent significant effort in modelling the effects of weather conditions and also extreme weather events on commuting and transport infrastructure. Also, prior research has tried to understand the role that Information and Communication Technologies (ICTs) can perform as an enabling platform for working remotely and avoiding or decreasing physical commuting. This PhD project will build upon these two streams of research and also incorporate a risk dimension which is related to extreme weather and climate change. For instance, changes to the daily commute can be made during extreme weather (e.g. floods, heatwaves, snow), allowing commuters to select the mode of transport which is most resilient for the conditions. At the extreme, telecommuting can be seen as a powerful tool to increase resilience.

Cities are organised in space as complex urban networks, which are connected together through various diverse layers of infrastructure (from transport to digital infrastructure). These infrastructural layers vary from city to city and will affect the capacity of individuals to commute. With respect to telecommuting, the complexity of the above argumentation increases if we consider labour and housing markets. For instance, not every industry can support and take advantage of telecommuting opportunities. Similarly, people whose occupation enables telecommuting may reside in close proximity or in areas of similar socio-economic profile. For instance, problems with Internet broadband connectivity in rural areas might still be a deteriorating factor for working from home and avoiding physical commuting. This PhD project will build upon the above narrative and answer research questions related with the capacity of places and individuals to telecommute, the relation of telecommuting with with weather and extreme weather events, and the link between infrastructure – both digital and transport infrastructure – with telecommuting.
Hannah Budnitz
Student:Hannah Budnitz

First supervisor: Dr Emmanouil Tranos

Email: e.tranos@bham.ac.uk
Telephone: +44 (0)121 414 2680
Commenced: October 2016
Supervisory panel: Birmingham Dr Emmanouil Tranos; Dr Lee Chapman (Birmingham)  
broadbandspeedchecker.co.uk
Partner: Broadband Speed Checker http://www.broadbandspeedchecker.co.uk

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.
Student: Marko Closs

First supervisor: 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

Cranfield University – DREAM Affiliates


Affiliated Doctoral Studentship projects  
For further details on these projects, contact Dr Stephen Hallett »
An integrated model of early warning system (EWS) of desertification in Libya
Azalarib Ali
Student: Azalarib Ali

First supervisor: Dr Stephen Hallett
Email: s.hallett@cranfield.ac.uk
Telephone: +44 (0) 1234 752750
Commenced: January 2014
Supervisory panel: Cranfield Dr Stephen Hallett; Tim Brewer   Research Sponsor Libyan Embassy, Cultural Attache, London

Assessing the current Status of the Natural Vegetation Cover in Al Jabal Al Akhdar Region, North East Libya, and Future Land Cover Management planning
Nagat M.G. Almesmari
Student: Nagat M.G. Almesmari

First supervisor: Dr Stephen Hallett

Email: s.hallett@cranfield.ac.uk
Telephone: +44 (0) 1234 752750
Commenced: July 2015
Supervisory panel: Cranfield Dr Stephen Hallett; Dr Rob Simmons   Research Sponsor Libyan Embassy, Cultural Attache, London

The use of remote sensing and GIS for monitoring rangeland degradation in Libya

Student: Abdulsalam A Al-Bukhari

First supervisor: Dr Stephen Hallett

Email: s.hallett@cranfield.ac.uk
Telephone: +44 (0) 1234 752750
Commenced: October 2015
Supervisory panel: Cranfield Dr Stephen Hallett; Tim Brewer   Research Sponsor Libyan Embassy, Cultural Attache, London