PhD Studentships
EPSRC Studentships
Applications for EPSRC Studentships are now closed for September 2022 entry.
Please see bottom of this page for Industry sponsored studentships currently available.
There are currently 10 fully-funded EPSRC PhD studentship awards available across Newcastle University and the University of Nottingham. Students are welcome to apply with their own research vision which we will help shape during the first year of the programme, our MRes in Geospatial Systems, with input from research teams at both Newcastle University and University of Nottingham. We also have a number of CDT Projects for direct applications.
Please visit our PhD Researchers page for details on topics currently being undertaken in the CDT.
We welcome applications from Home and International (incl. EU) students from a range of backgrounds, including Computing, Engineering, Geography, Geomatics, Mathematics and related disciplines, and are fully committed to equality, diversity and inclusion to ensure fair opportunity for all. Please note that there are a limited number of international awards available.
Sucessful applicants will receive full fees and an annual living allowance which is based on the UKRI 2022/23 rate (estimated minimum award £15,840). A substantive Research Training Support Grant (RTSG) of £15,000 to cover the costs for consumables, travel, conferences, and access to placement opportunities with industrial partners.
Applicants wishing to be considered for an EPSRC studentship should apply directly to Newcastle University or University of Nottingham quoting the relevant studentship code on their application and supporting documents. (see How to apply).
For general enquiries about CDT studentships, email geospatial.cdt@newcastle.ac.uk
Industry Studentships
Modelling strategies for rail network predictability, human performance and chaotic behaviour appraisal - Network Rail
Closing Date, Monday 6th June 2022
Please quote project application reference: GEO22_NW
Minor disturbances in the performance of train services can have a cascading effect on overall system performance. Being able to predict the impact of delays is essential. However, the quality of predictions is variable.
Network simulation is one way to predict network performance, but current approaches have several issues:
- Simulation efforts struggle to integrate multiple simulation models being used.
- Human performance has a crucial impact on overall network performance and predictability, but is rarely modelled or accurately accounted for in simulation efforts;
- There is a lack of detailed evaluation of the objective assessment of the quality of current predictions against actual network performance.
- The behaviour of the network, or meaningful sections of it, is challenging to model with sufficient levels of confidence as it is highly complex and potentially chaotic in nature;
- Any solution must be scalable, both to different parts of the network, and to look at network integration as a whole.
During the first year, as part of the MRes in Geospatial Data Science, this project will carry out preliminary background research to define the underpinning modelling approaches used to predict rail network performance, including identifying the parameters used, their relevance and the data sources that provide them. This will allow you to assess the predictive accuracy of current practice for forecasting network performance, including data quality and machine learning. You will also perform a preliminary Assessment of the potential of considering rail network prediction as a chaotic problem. You will apply your machine learning and visualisation skills to formulate a hypothesis on how these techniques (e.g. ML and visualisation) could transform predictability accuracy, including gaps in the parameters used. The findings from this MRes will inform the research design of the subsequent PhD programme.
Applicants should have a good (2:1 or better) undergraduate or masters (merit or better) degree in computing science, mathematics, physics, engineering or related disciplines. Programming skills (e.g. in Python or C++) are desirable but not essential as training in programming will be provided via the MRes taught component and support will be available throughout the research programme. Skills and knowledge of complex network modelling and/or railway systems is desirable but not essential. Unfortunately, this project is only available to UK home students.
This project will be co-supervised by Network Rail.
For more information, please contact Dr Roberto Palacin roberto.palacin@ncl.ac.uk
Self-Adaptive Vehicle Tracking using Transfer Learning and Bayesian Inference within Smart Cities - Defence Science and Technology Laboratory (DSTL)
Closing Date, Monday 6th June 2022
Please quote project application reference: GEO22_DSTL
Deep Neural Networks (DNNs) play a pivotal role in data-driven decision-making within smart cities, and vehicle tracking provides attractive opportunities for their application, especially in combination with data from the Internet of Things (IoTs).
This project aims to develop an innovative transfer learning framework to accommodate the heterogeneity of vehicle tracking within smart cities. This aim will be achieved via three objectives: (1) modelling spatial autocorrelation of IoT sensors using spatial tessellation techniques; (2) producing the re-training datasets using the Bayesian inference-based data generative models; (3) design and development of the new transfer learning framework based on (1) and (2).
During the first year, as part of the MRes in Geospatial Data Science, the research will focus on the investigation of state-of-the-art deep neural networks and a literature review in vehicle tracking using deep learning techniques is expected. Following this MRes work, the PhD research, both spatial analytics and Bayesian inference will combine with deep learning to design and develop the new transfer learning framework for real-time vehicle tracking using IoT sensor networks.
A successful applicant will have a minimum of a 2:1 undergraduate degree (or equivalent) in Computing, Engineering, Geography, Geomatics, Environmental Sciences, Mathematics and Statistics, or related disciplines. Unfortunately, this studentship is only available to UK students. Unfortunately, this project is only available to UK home students.
This project will be co-supervised by Defence Science and Technology Laboratory
For more information, please contact Dr Jin Xing, jin.xing@ncl.ac.uk


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