Towards Reliable AI under Domain Shift: Uncertainty-Aware Learning

Towards Reliable AI under Domain Shift: Uncertainty-Aware Learning ThreapL Tue, 13/10/2026 - 10:16 Funding Self-funded Department School of Computing, Mathematics and Physics Faculty Faculty of Technology Research areas Computing Award PhD Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project. The PhD will be based in the School of Computing, Mathematics and Physics and will be supervised by Dr Dongxu Gao. Modern AI systems in computer vision achieve strong performance under controlled conditions but often fail to generalise in real-world environments due to domain shift, where data distributions differ across settings. This limits their reliability and safe deployment. The work on this project will: Develop reliable AI models that remain robust under domain shift in real-world imaging; Investigate uncertainty-aware learning to enable models to recognise and quantify their limitations; Explore adaptive inference strategies (e.g., test-time adaptation) for dynamic environments; Opportunity to work on cutting-edge trustworthy AI with strong potential for high-impact publications. This PhD project aims to develop reliable AI models for general imaging tasks that remain robust under such distributional changes. The research will focus on integrating uncertainty-aware learning, domain generalisation, and adaptive inference to enable models to recognise their limitations and respond effectively to new environments. The project will initially investigate these challenges in general computer vision tasks (e.g., recognition, segmentation), with methodologies designed to be broadly applicable. As the research progresses, the approaches may be extended to high-impact domains such as medical imaging, where robustness and trustworthiness are essential. Artificial intelligence (AI) models for image analysis have achieved strong performance in tasks such as classification and segmentation. However, these models often fail to generalise in real-world environments due to domain shift, where data distributions differ across settings (e.g., changes in sensors, environments, or acquisition conditions). This limitation poses a major challenge for the reliable deployment of AI systems. This PhD project aims to develop reliable and uncertainty-aware AI models for general imaging tasks that can operate robustly under such distributional changes. The research will focus on integrating uncertainty estimation, domain generalisation, and adaptive inference strategies, enabling models to quantify their confidence, recognise potential failure cases, and respond effectively to new environments. The project will initially investigate these challenges in general computer vision problems, such as object recognition and segmentation across multiple domains, to develop broadly applicable methodologies. It will explore approaches that go beyond standard supervised learning, including techniques for improving generalisation from limited data and lightweight adaptation at test time. The outcomes are expected to contribute to the development of trustworthy and deployable AI systems, with potential extension to high-impact domains such as medical imaging. Fees and funding Visit the research subject area page for fees and funding information for this project. Funding Availability: Self-funded PhD students only. PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only - eligibility criteria apply). Bench fees Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable. Entry requirements You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Computing or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0. International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office. How to apply We’d encourage you to contact Dr Dongxu Gao (dongxu.gao@port.ac.uk) to discuss your interest before you apply, quoting the project code. When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake.. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. When applying please quote project code CMP10570529. Contact information Admissions +44 (0) 23 9284 5566 Contact Admissions Postgraduate Open Days and events Come to one of our events to explore your course, find out more about our expert support teams and talk to our current students. Book your place Project Code CMP10191026 Start dates October, February and April Application deadline description Applications accepted all year round Display navigation Off Exclude from internal search Application deadline date Thu, 31/05/2029 - 12:00