PhD Artificial Intelligence Machine Learning and Advanced Computing

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, 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.

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.