Making your application to DREAM


New fully-funded NERC research PhD studentships available in DREAM

We are pleased to announce a new set of fully funded PhD studentships in the NERC DREAM CDT – the details are below! If you are interested in joining us in DREAM to study for a PhD in Big Data, Risk and Environmental
Analytical methods, we look forward to hearing from you. Please take note of the University proposing each of the projects and be sure to contact the respective team regarding any further details you need.

We have 7 new fully funded positions open for the right applicants, to commence in October 2017, to be drawn from across the following list of project proposals of the partner universities. Any questions? See our FAQ, or do get in touch. See also the timeline of the application process.


MS Word document
DREAM CDT Student Application Form »

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



Cranfield University-based projects
For these projects, send completed applications to Dr Stephen Hallett
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.

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

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

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

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

If you want to help save water, save energy and save the loss of water supply to thousands of people through the use of environmental data science, read on.

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.

Where you come in:
Through your research you will 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. You will:

  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

Your 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. You will be working in an active, and truly supportive research team. We are closely aligned to industry, and seek to better our understanding of the dynamic interactions between soil, weather and infrastructure.

Through the course of this PhD you will develop big data skills which enable you to clean, process and use 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. You will discuss and integrate your research with front-line infrastructure operators, so they can improve their service through the use of your results.

By the end of the PhD programme you will have developed highly transferable skills to become a leading force in environmental data science, You will have developed a network of industry contacts to ensure your science is relevant enough to bring lasting benefit. These contacts and skills will also open the doors to rewarding and stimulating careers in industry and academia. No one wants to be stuck in a boring, irrelevant, job. This PhD offers you a chance to develop skills and relationships to help ensure you never will be.

If using environmental science to better the management of our limited natural resources excites you, we strongly encourage you to apply for this PhD.

[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)

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

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

Reducing water leakage through the development of high resolution soil maps

Do you want to use your skills and abilities to bring lasting change to the world? Do you want to develop skills so you can use environmental data science to improve both infrastructure and the environment? If so, this could be the PhD for you.

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.

Where you come in:
You will address these problem through delivering the three components of this PhD:

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

Through your work 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. You will be working in an active, and truly supportive research team. We are closely aligned to industry, and seek to better our understanding of the dynamic interactions between soil, weather and infrastructure. Through your research, we will be able to improve our ability to benchmark and improve network performance. You will be a key member of this team, developing new digital soil mapping techniques to enhance our understanding of the soil. You will work alongside others to integrate these new maps into predictive burst models, and use the outputs 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.

By the end of the PhD programme you will have developed highly transferable skills to become a leading force in environmental data science, You will have developed a network of industry contacts to ensure your science is relevant enough to bring lasting benefit. These contacts and skills will also open the doors to rewarding and stimulating careers in industry and academia. No one wants to be stuck in a boring, irrelevant, job. This PhD offers you a chance to develop skills and relationships to help ensure you never will be.

If using environmental science to better the management of our limited natural resources excites you, we strongly encourage you to apply for this PhD.

[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)

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

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

Understanding the impact of future development schemes on the natural environment

The South East of the UK is one of the densely-populated areas of Europe. The demands on infrastructure, water needs and urban development place particular pressures on greenspace and natural environment. What is still missing are a set of tools which can assess the impact of these planned developments on the natural environment and which will allow decision makers evaluate the possible benefits as well as impacts of particular development schemes (e.g. greenspaces, eco-developments, better water infrastructure). This PhD will seek to develop these tools, describing the changes in ecosystem goods and services as various development schemes or infrastructure plans are assessed. For these tools to be practical and useful to industry, they need be both reflective of the underlying natural processes but also easy and time efficient in their use. Interacting closely with our Industrial partners, the PhD will apply these tools to current development schemes in the South East, ensuring that the research is exciting, current and immediately applicable to contemporary societal needs.

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

For further details: Please contact Dr Ronald Corstanje (Cranfield), Senior Lecturer in Environmental Informatics
Email: roncorstanje@cranfield.ac.uk
Telephone: +44 (0) 1234 752765

Professor Jim Harris (Cranfield), Professor of Environmental Technology
Email: j.a.harris@cranfield.ac.uk
Telephone: +44 (0) 1234 758067

Industrial partner: Mott MacDonald

Data fusion for real-time decision support in excavation
Underground tunnel connecting the pipes from the old plant to the new plant for transport gas and electrical line.

Underground tunnel connecting the pipes from the old plant to the new plant for transport gas and electrical line.

Fusion of data from multiple sources and real-time consolidation for decision support is the challenge for this doctoral research. Spatially aware data from excavation sites from multiple types of sensor will be consolidated together with previously digitised plans of buried utility assets, to provide state of the art decision support to excavator operatives. Avoiding cable strikes and water main bursts will avoid socio-economic disruptions but improved health and safety and reduced environmental impact from avoided congestion, pollution, etc. are equally important objectives. The use of transformative digital technologies including super-computing and advanced modelling are anticipated and the opportunity to examine robotics and artificial intelligence on the way toward semi-autonomous excavators. The research is located at the intersection of digital (ICT) and transport infrastructures and has the potential to drive growth across the whole country when solutions are adopted by OEMs and construction companies. It will also make a significant contribution to upgrading infrastructure given the rising densification and growth of cities. Industrial and professional support will be provided by Highways England, Transport Systems Catapult and multiple partner institutions.

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

For further details: Please contact Professor Liz Varga (Cranfield), Professor of Complex Infrastructure Systems
Email: liz.varga@cranfield.ac.uk
Telephone: +44 (0) 1234 754802

Industrial partner: Highways England

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.

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

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.

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

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)

NERD-IT: NEw forms of Real time Data informing Innovative Tariffs
Achieving low carbon transition will require high shares of renewable energy generation on the one hand and higher demands for electricity due to electrification of heating and transport sectors on the other. In order to reduce the need for costly stand-by capacity and keep the cost of energy affordable, the energy industry needs to be able to articulate what is happening in real or near real time in demand and supply profiles, how they might change next and what actions should be taken to lower costs of energy provision. The roll-out of smart meters along with the increasing availability of new forms of user data from crowdsourced platforms such as social media, mobile phones and apps offers an immense opportunity to improve our understanding of consumer’s energy behaviours and preferences and UK’s changing energy mix in near real-time and at a low geographical resolution.

This PhD studentship will involve collaboration with Centrica’s Cornwall Local Energy Market project, an ERDF-funded project that will test the use of flexible demand, generation and storage, and reward local people and businesses for being more flexible with their energy. The PhD student will analyse the scope of computational methods like machine learning or hierarchical analytic methods to analyse these new forms of data to enable suppliers to recognise tipping points, emerging patterns, interdependencies and end-user behaviours in near real time. This would in turn enable the development of demand side measures in the residential sector and reduce the need for stand-by generation, overall keeping energy affordable for UK consumers whilst decarbonizing our energy system.

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

For further details: Please contact Dr Nazmiye Ozkan (Cranfield), Senior Lecturer in Environmental/Energy Economics
Email: n.ozkan@cranfield.ac.uk
Tel: +44 (0)1234 754296

Industrial partner: Centrica

SAPPER - A Systems APproach to infrastructure investment planning: Developing robust methodologies for business Planning under Extreme Risk, uncertainty and complexity
There is significant ongoing need to invest in resilient and new forms of infrastructure to meet the challenges of climate, social and technological change. However, traditional approaches to investment planning struggle to value extreme risk (low probability, high impact risks), uncertainty and complexity. As a result, key infrastructure investments that would reduce our vulnerability to extreme risks may not be made. Without a robust tool set to assess the value of infrastructure investments that mitigate extreme risk we may see an increasing vulnerability of infrastructure assets, and therefore of society and the environment. As the UK is entering an important period of national infrastructure renewal that will transform some of our critical national infrastructure including energy, transport and finance, this PhD research will be timely and important. Challenges include:

  1. The volume and diversity of data generated by impact assessments mean that incorporating quantitative risk assessments and scenario analysis within a single decision-making framework is challenging.
  2. Low probability high impact hazards and failure modes are not routinely considered in quantitative risk assessments.
  3. The system complexity caused by increasing interdependencies between infrastructure and within infrastructures such as electrical and gas distribution networks.
  4. The common approaches to cost benefit and net present value analysis tend to emphasise mean or expected values while undervaluing extreme value risks (low probability, high impact) and ignore uncertainty.

Working in partnership with Business Modelling Associates (BMA), the Cranfield Institute for Resilient Futures and Complex Systems Research Centre, this exciting studentship develop extreme value analysis techniques to allow for the accounting for extreme risk in business planning and investment decision-making. The studentship will use real life case studies provided by BMA’s network on infrastructure clients to develop a methodology and tools to support decision makers, enabling them to account for extreme risk (low probability high impact risk events), uncertainty, data paucity, and system complexity in the context of infrastructure investment planning. Dr Simon Jude and Professor Liz Varga of Cranfield University, and Dr Craig Mauelshagen of BMA will supervise the student who will be based at BMA in Bradford. BMA will provide training in their state of the art systems modelling software.

We are seeking a highly motivated candidate with an interest and aptitude for transdisciplinary risk research, particularly complex infrastructure and environmental hazards and risks, modelling, economics and decision support. The candidate will be a member of the NERC/ESRC DREAM Centre for Doctoral Training and will receive excellent support in terms of training and mentoring via the DREAM CDT, and Cranfield’s School of Water, Energy and Environment, and School of Management.

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

For further details: Please contact Dr Simon Jude (Cranfield), Lecturer, Cranfield Institute for Resilient Futures
Email: s.jude@cranfield.ac.uk
Tel: +44 (0)1234 754295

Industrial partner: Business Modelling Associates


Birmingham University-based projects
For these projects, send completed applications to Dr Emmanouil Tranos
Real-time integrated modelling of transport-related air pollution in urban street networks: Risk assessment and policy evaluation.

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

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

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

Industrial partners Amey


Newcastle University-based projects
For these projects, send completed applications to Dr Stuart Barr
Big data tools for distributed environmental hazard data lakes
Data Lakes Increasingly institutions and organisations need to collaborate efficiently in order to address the environmental risks faced from natural hazards (e.g., floods, heatwaves, landslides etc.), and in order to develop robust mitigation policies in response to these. Such collaboration is increasingly concerned with understanding the risks posed by multiple environmental hazards across unprecedented spatial and temporal scales (e.g. from the local scale through to continental; and daily through to decadal). In such studies, the efficient management of data becomes a major issue, particularly where huge volumes of environmental data on hazards, as well as vulnerability and exposure needs to be managed across/between multiple collaborating institutions.

This PhD will utilise Big Data approaches to develop and implement a framework for distributed environmental risk data management. It will develop the informatics approaches, comprising the data management software (proprietary and open source) and related database architecture required to allow the virtual integration of multiple distributed data stores from different intuitions and organisations. The research will develop the interfaces required by environmental scientists to access data sets from different institutions in a seamless manner on the basis of shared/common data characteristics such as, for example, spatial and temporal coincidence. The tools developed in this research will be demonstrated by developing a DREAM consortium-wide distributed environmental data management system for the DREAM PhD projects and associated research projects. During the course of this PhD the successful candidate will work with DREAM projects in order to design and develop a set of tools that provide maximum utility. Existing data on environmental risk at DREAM institutions as well as data arising from NERC data centres such as the Data Catalogue Service (DCS) and Environmental Information Data Centre (EIDC) will be made available to this project. Partner ESRI (UK) Ltd will make available to the researcher the latest suites of big data analytical software tools from ESRI, providing appropriate support to assist in developing their application and usage alongside other open source informatic tools.

About you: Applicants should hold a minimum of a UK Honours degree at 2:1 level in a subject such as Computer Science, or Geographical Information Science. Experience of high-level programming and its application to spatial/temporal data is highly desirable.

For further details: Please contact Dr Stuart Barr:

Email: stuart.barr@ncl.ac.uk
Telephone: +44 (0) 191 208 6449
Supervisory panel: Dr Stuart Barr (Newcastle); Dr Raj Ranjan (Newcastle); Dr Stephen Hallett (Cranfield)

Timeline and what happens next

  1. Studentship advertisements are announced – Tuesday 16th May, 2017
  2. National advertisements released – Monday 22nd May, 2017
  3. Studentship applications closing date – Friday 30th June, 2017
  4. Notification of applicants selected for interview – Friday 30th June, 2017
  5. Interviews held – between Monday, 3rd to Friday, 14th July, 2017
  6. Notification of successful applicants – Tuesday, 18th July, 2017
  7. DREAM induction – October, 2017

Background: Overall, the DREAM Centre for Doctoral Training supports three cohorts of 10 students, who join our programme in 2015, 2016 and 2017 respectively, plus these newly announced seven places. There are a number of other PhD students whose studies are affiliated to DREAM – extending our vibrant community of research practice.

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

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

I am interested – what do I need to do?

The full selection of proposed doctoral projects is listed above, and is also available in other appropriate online channels, as well as on respective university noticeboards. You can make your application for up to two specific projects – in fact doing so may aid your chances. The DREAM student application form is provided above for this purpose.

What are the benefits accruing from DREAM?

Over the course of their three year study with us (full time), DREAM students all receive the following annual bursary from NERC, made as a tax free payment:
Year 1: £17.5k
Year2: £18k
Year 3: £18.5k
Sum: £54k
In addition Dream students will have their university fees automatically paid, and their studentship is also allocated a ‘Research Training Support Grant’ (RTSG), managed through the centre that provides students access to a wide spectrum of training and development activities, and to inter-institute events such as our annual DREAM Challenge Week and the DREAM Symposium. Further to this, and the full range of advanced technical and scientific training of offer, our current DREAM students have also received a range of expert instruction and training, supported by NERC. In the past this has included week-long sessions at the renowned Hartree Centre for Big Data, an ‘Ideas to Impact’ course in the Cranfield School of Management, and a science communication course with professional graphic designers. Full details of these events are elsewhere on this site. More exciting training and development activities are planned!

I need more information

Please identify and contact one of the DREAM Management Board representatives who will be pleased to advise further.

Residence requirements and Eligibility

The ‘Research Councils UK’ (which incorporates NERC) have rules on Funding for Research Training. This webpage links to the Terms and Conditions of Research Council Training Grants which, in Clause 43 onwards, notes that to be eligible for a full award a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. To be eligible for a ‘fees only’ award, note that students from EU countries other than the UK are generally eligible where they have been ordinarily resident in a member state of the EU; in the same way as UK students must be ordinarily resident in the UK. If you seek clarification on your situation, please contact us.

Note that for purposes of residence requirements, the UK includes the United Kingdom and Islands (i.e. the Channel Islands and the Isle of Man).

I have a disability, what should I do?

Described at Funding for Research Training, the Research Councils UK (RCUK) Disabled Students’ Allowances (DSA) are intended to help with additional expenditure for the costs of study-related requirements that may be incurred as a result of disability, mental health problem or specific learning difficulty that means additional support is needed to undertake a Research Council funded studentship. The allowances can cover the cost of non-medical personal assistance, items of specialist equipment, extra travel costs and general expenses. Please contact us for further information.

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