About the Project
AI_CDT_DecisionMaking
Details
Modern scientific facilities produce more data than humans can inspect directly. This PhD asks when a foundation model should act, when it should be uncertain, and when it should ask an expert. Using a 91-million-parameter model already running on real neutrino-detector data, you will develop uncertainty, anomaly-detection and human-in-the-loop methods for scientific systems that change over time, with applications from DUNE to astronomy and other data-intensive science.
Desirable Student Background:
A first or upper-second class degree (or equivalent Master's) in computer science, physics, mathematics, data science or a related discipline, with strong programming skills in Python. Experience with machine learning, for example PyTorch, would be valuable. Prior knowledge of particle physics is not required; the scientific context and detector physics can be learned within the project. The project would particularly suit a student interested in foundation models, uncertainty quantification, representation learning or human–AI decision-making who wants to develop those methods on complex real-world data.
How to apply:
Please apply under the University of Manchester application portal and select 'PhD in Artificial Intelligence'.
When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.
Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
You will be asked to upload the following supporting documents:
- Final Transcript and certificates of all awarded university level qualifications
- Interim Transcript of any university level qualifications in progress
- CV
- Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
- Contact details for two referees (Please ensure referee email addresses are official university or workplace accounts, as we may need to verify the references.)
- English Language Certificate (if applicable)
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants.
We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs, including a part-time study option.
Join us and help grow the UK’s national capability in artificial intelligence, part of the UK’s modern industrial strategy. Everyone is welcome if you are passionate about uncertainty quantification, decision making with humans in the loop and decision making for machine learning systems and their applications in Science and Engineering, and looking to develop your research and innovation skills at the doctoral level to further your career in the AI sector.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
Funding Notes
Fully funded AI UKRI CDT 4 year program; The Project will be based in University of Manchester.
The UKRI AI CDT in Decision Making for Complex Systems is part of the TechFirst offering stipends of £30,000 for students eligible for home fee status. The start date is September 2026.
References
X. Wu et al., “Sonata: self-supervised learning of reliable point representations”, CVPR 2025. arXiv:2503.16429.
X. Wu et al., “Point Transformer V3: simpler, faster, stronger”, CVPR 2024. arXiv:2312.10035.
V. Da Silva, “Performance of LoRA fine-tuning on Sonata pre-trained LArTPC (MicroBooNE) Point Cloud Network”, talk at NPML 2026, SLAC, June 2026.
S. Young, Y.-J. Jwa and K. Terao, “Particle trajectory representation learning with masked point modeling”, Mach. Learn.: Sci. Technol. 7 (2026) 025023. arXiv:2502.02558.
S. Young and K. Terao, “Panda: self-distillation of reusable sensor-level representations for high energy physics” (2025). arXiv:2512.01324.
S. Alonso-Monsalve et al., “Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training” (2026). arXiv:2604.07037.
A. Hallin, “Foundation models for high-energy physics” (2025). arXiv:2509.21434.
B. Lakshminarayanan, A. Pritzel and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles”, NeurIPS 2017. arXiv:1612.01474.
A. N. Angelopoulos and S. Bates, “A gentle introduction to conformal prediction and distribution-free uncertainty quantification” (2021). arXiv:2107.07511.
MicroBooNE Collaboration, “The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector”, Eur. Phys. J. C 78 (2018) 82. arXiv:1708.03135.
DUNE Collaboration, “The DUNE far detector vertical drift technology: technical design report”, JINST 19 (2024) T08004. arXiv:2312.03130.
Links: https://www.dunescience.org · https://microboone.fnal.gov · https://sbn-nd.fnal.gov