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. We are now recruiting a third cohort. The research projects that DREAM is currently supporting are outlined below.
| NEW The 2017 DREAM PhD studentships are now available – we look forward to hearing from you.
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Also presented are some video clips showing was it is like being a DREAM student.
|Cranfield University-based projects
For further details on these projects, contact Dr Stephen Hallett »
|The impact of tree-related ground movement on water infrastructure
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.
First supervisor: Dr Timothy Farewell
|Coastal management and adaptation an integrated big data approach - improved risk based decision-making
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.
First supervisor: Dr Stephen Hallett
|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.
First supervisor: Dr Stephen Hallett
|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.
Student: William Rust
First supervisor: Professor Ian Holman
|Near real-time correction of flood forecasting using high resolution satellite data for multi-hazard risk assessment in lowland tropical regions
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.
First supervisor: Dr Bob Grabowski
|Newcastle University-based projects
For further details on these projects, contact Dr Stuart Barr »
|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,
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.
First supervisor: Professor Richard Dawson
|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.
First supervisor: Dr Geoffrey Parkin
|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.
First supervisor: Professor Philip Moore
|Multi-objective spatial risk and sustainability optimisation tools using cloud computing
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.
Student: Grant Tregonning
First supervisor: Dr Stuart Barr
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
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.
First supervisor: Dr Nigel Collier
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.
First supervisor: Dr Pietro, Lio’
|Temporal and spatial patterns of shoreline change and exposure of coastal communities and ecosystems to future flood risk
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.
First supervisor: Dr Thomas Spencer
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
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?
First supervisor: Dr Stephanie Handley-Sidhu
|Risk of landscape failure examined through big data analysis; implications of Oil Sand mine closure planning
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.
First supervisor:Dr Nick Kettridge
|(Tele)commuting, cities and weather conditions
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.
First supervisor: Dr Emmanouil Tranos
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|
|Assessing the current Status of the Natural Vegetation Cover in Al Jabal Al Akhdar Region, North East Libya, and Future Land Cover Management planning|
|The use of remote sensing and GIS for monitoring rangeland degradation in Libya|