About This Course
Three fully-funded 4-year PhD scholarships are available to start in October 2021 in the area of Artificial Intelligence machine learning and advanced computing. The PhDs are suitable for graduates with a keen interest in AI algorithms for data analytics, visualisation and image analysis.
The 4-year PhD scholarships, will sit within the UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing (CDT-AIMLAC, http://cdt-aimlac.org/). The two students will be based at Bangor University, located within the School of Computer Science and Electronic Engineering (CSEE). Funding will cover the full cost of UK/EU tuition fees and an annual stipend of £ 15,285. Additional funding is available for research expenses.
Candidates must identify their preference of at least two projects (indicating clearly the primary supervisor and title) from the following. Additional information of the projects can be found here.
- Edge-based object recognition for immersive analytics in Web-based XR. Supervisor: Dr Panagiotis (Panos) Ritsos (CSEE). Second supervisor: Professor Jonathan C. Roberts (CSEE). This research will investigate the use of edge-based object recognition using distributed neural networks (DNN), as a mechanism for in-situ registration and data processing for mobile, Web-based Immersive Analytics (IA) in Extended Reality (XR).
- FLOOD-AI: Using Artificial Intelligence to Investigate the Impact of Land Management Decisions on River Flood Risk. Supervisor: Dr Sopan Patil ( School of Natural Sciences). Second supervisor: Dr Panagiotis (Panos) Ritsos (CSEE). This research will develop AI techniques that can help improve the ability of hydrological models to predict the impact of land use change on river flood risk. The approach will involve development of Deep Learning techniques to extract high level abstractions in the hydrological model and physical river basin data.
- Predicting the “Relative” Coastal Weather and Conditions. Supervisor: Peter Robins (School of Ocean Science). Second supervisor: Matt Lewis (School of Ocean Science). This research will investigate how Artificial Intelligence algorithms and ANN (artificial neural networks) can be used to develop a novel Met Ocean prediction tool integrating user confirmatory feedback.
- Ensembles of Deep Neural Networks for Semi-supervised Learning. Supervisor: Prof Ludmila Kuncheva (CSEE). Second supervisor: Dr Franck Vidal (CSEE). In semi-supervised learning, some of the data have labels but most of the data is unlabelled. Ensemble models are known to be more accurate than single models. This research will investigate the need for a Deep Learning Neural Networks (DLNN) ensemble based on data size and characteristics, examine the contribution of diversity within the DLNN ensembles.
- Bringing big-data to social science. Supervisor: Dr Simon Willcock (School of Natural Sciences). Second supervisor team: Dr William Teahan (CSEE) and Prof Jonathan Roberts (CSEE). Using existing data from seven national surveys across Wales (c. 1000 respondents per survey) and computing with Supercomputing Wales, we will use bespoke Natural Language Processing (NLP) to analyse the quantitative data within these surveys, understanding how people’s reasons for spending time in greenspace change from before, during and after the ongoing coronavirus crisis
- Optimization of co-located offshore wind and wave energy arrays. Supervisor: Prof Simon Neill (School of Ocean Sciences), Dr David Christie. Co-locating wave energy with offshore wind developments leads to synergies which can improve efficiency and reduce cost. This project will apply genetic algorithms to determine the optimal device configuration. The student will use a coupled wind/wave resolving model running on a Supercomputing Wales to determine the effect of WECs and wind turbines on the surrounding wave and wind fields.
- Artificial Intelligence to engender Fast Visualization Ideation Design. Supervisor Professor Jonathan C. Roberts (CSEE). Second supervisor Dr Panagiotis (Panos) Ritsos (CSEE). Imagining and developing new creative visualizations takes time. This research will investigate current visualization design strategies such as the Five Design-Sheet method, multiple views, deep learning, ideation techniques, recommender systems, and version control, to guide the user in their design and idea creation: making the design creation process smart.
The successful candidates will be required to attend taught components in year 1 (such as foundations of AI, research methods, information visualisation), residential meetings at Aberystwyth, Bristol, Cardiff or Swansea Universities, deliver responsible innovation, and engage with placements with external partners throughout the four-year programme. Placements may be six-month, or shorter three-month or two-week blocks. Successful applicants will be registered at Bangor University, hosted by the School of Computer Science and Electronic Engineering throughout their period of study, with the delivery of the related training in the PhD programme being shared between the Universities of Aberystwyth, Bangor, Bristol, Cardiff and Swansea.
Applicants should have at least a 2:1 degree. Applicants must demonstrate excellent programming skills, and have followed a suitable degree programme, e.g., in computer science, mathematics or electronic engineering (with substantial programming), or closely related discipline. Applicants must have an interest in AI, machine learning and advanced computing and one of the topics, above. Applicants must have excellent written and spoken English (IELTS 6.5). Applicants should have an aptitude and ability in computational thinking and methods (as evidenced by your degree). Shortlisted candidates will be interviewed around the second half of February to the beginning of March.
We encourage UK and overseas applicants. To qualify as a UK applicant, prospective students must have been ordinarily resident in the UK for three years immediately prior to the start of the award, with no restrictions on how long they can remain in the UK. Overseas applicants are eligible, fully funded, and count towards a quota that is applied across the whole AIMLAC CDT.
More information on this exciting research can be found here.
Follow the three-part process.
- Complete Bangor’s electronic application process
- E-mail the PDFs of your application to firstname.lastname@example.org, indicating your choices of projects and supervisor.
- Complete the online equality, diversity and inclusivity form.
For Bangor’s electronic application process, applicants must include the following attachments in PDF form:
- Degree certificates and transcripts (if you are still an undergraduate, provide a transcript of results known to date)
- A statement no longer than 1,000 words that explains why you want to join our Centre, and your top two preferred projects (title and supervisor). Academic references - all scholarship applications require two supporting references to be submitted. Please ensure that your chosen referees are aware of the funding deadline (to be determined), as their references form a vital part of the evaluation process. Please include these with your scholarship application.
Interviews (using video conferencing or in person) will occur during the second half of February to the beginning of March.
The deadline for applications is February 12th 2021; however applications will be accepted until all positions are filled.
For more information please contact Professor Jonathan Roberts