Probabilistic modelling and Bayesian machine learning (2025 entry onward)
Primary supervisor
Additional information
- J??lk?? et al. (2021) Privacy-preserving data sharing via probabilistic modeling. Patterns 2:100271.
- Hegde et al. (2019). Deep learning with differential Gaussian process flows. AISTATS (2019), notable paper award.
- Lintusaari et al. (2018). ELFI: Engine for likelihood-free inference. Journal of Machine Learning Research 19(7):1-7.
- My most up-to-date publication list is at:
Contact admissions office
Other projects with the same supervisor
- Trustworthy Multi-source Learning (2025 entry onward)
- Automatic Experimental Design with Human in the Loop (2025 entry onward)
- Learning of user models in human-in-the-loop machine learning (2025 entry onward)
- Collaborative Probabilistic Machine Learning (2025 entry onward)
Funding
- Directly Funded Project (UK Students Only)
This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.
Project description
I am looking for a doctoral student to join my new Manchester group to work on new probabilistic models and inference techniques. I have exciting research topics available around the following areas, and I am also open to new suggestions: (1) simulator-based inference, for combining first-principles models with learning from data, (2) Bayesian deep learning, (3) Bayesian reinforcement learning and inverse reinforcement learning, (4) with multi-agent modelling, and (5) privacy-preserving machine learning and synthetic data generation. Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications, including user interaction, health, medicine, synthetic biology and digital twins.
Person specification
For information
- Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
- Candidates must meet the School's minimum English Language requirement.
- Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
Essential
Applicants will be required to evidence the following skills and qualifications.
- You must be capable of performing at a very high level.
- You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.
Desirable
Applicants will be required to evidence the following skills and qualifications.
- You will have good time management.
- You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
General
Applicants will be required to address the following.
- Comment on your transcript/predicted degree marks, outlining both strong and weak points.
- Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
- How well does your previous study prepare you for undertaking Postgraduate Research?
- Why do you believe you are suitable for doing Postgraduate Research?