Research Directions
My group's primary research aim will be to explore how the geometry of data can help build better uncertainty-quantifying models, particularly for complex data types like molecules and images. Nonetheless, all areas within geometric learning and uncertainty quantification fall within our scope.
PhD Opportunities
You can apply for a PhD position under my supervision through various scholarships or one of the Centres for Doctoral Training (CDTs) 
if your research proposal relates to geometric learning or uncertainty quantification.
PhD Scholarships
I support applications for various PhD scholarships. You can find a list of scholarships available at the University of Edinburgh here.
Centres for Doctoral Training (CDTs)
I support projects for the following CDTs:
- Machine Learning Systems CDT
- Responsible NLP CDT (for this one, you will need to find a co-supervisor specializing in NLP)
- CDT in Dependable and Deployable AI for Robotics
- CDT in AI for Biomedical Innovation (if there is a project I currently propose on their website)
Other
I do not currently accept new applications for the Fully-funded PhD in Geometric Learning and/or Uncertainty Quantification call, funded by my personal startup grant. However, I am still processing the submitted applications. You can view the details of the call by clicking expand / hide details.
Fully-funded PhD in Geometric Learning and/or Uncertainty Quantification
Join our new research group at the University of Edinburgh, focusing on geometric learning and uncertainty quantification!
Our primary research aim will be to explore how the geometry of data can help build better uncertainty-quantifying models, particularly for complex data types like molecules and images. However, other research directions within geometric learning and uncertainty quantification will also be available for you to explore. This PhD position offers a unique chance to influence the trajectory of our group's work and contribute significantly to these growing fields.
This PhD position is ideal for candidates interested in the following areas of machine learning:
- Models that leverage the inherent structure of data, whether explicit or implicit [Geometric Learning]
(to improve data-efficiency and satisfy constraints) - Models that quantify uncertainty associated with predictions [Uncertainty Quantification]
(to support model-based decision-making techniques and safety-critical applications)
When: Ideally, starting September 2025.
Where: School of Informatics, the University of Edinburgh, one of the top computer science departments globally [1, 2]. More specifically, you will be a part of the Institute for Adaptive and Neural Computation, a vibrant community of researchers in machine learning and related areas.
The program lasts 3.5 years. It is straight to research, meaning no coursework. Teaching is optional. If you teach, you get additional money for it.
Funding: The position is fully-funded. Funds cover all tuition fees, and a standard UKRI-rate stipend. You will also have access to the common travel funds of the institute. Note that stipends are not taxed in the UK. Also, PhD students do not pay council tax, i.e. rent is a bit cheaper for you.
Overall, the stipend should be sufficient to live rather comfortably in Edinburgh, see, e.g., this reddit thread.
Requirements: Python programming skills, solid mathematical background. An international equivalent of the UK 2:1 honours degree (i.e. bachelor's degree with high-enough GPA) in computer science, mathematics, or a related discipline. A master's is desirable but not required. A proof of English language proficiency (can be postponed until after the offer, if so the offer will be conditional on receiving an appropriate proof). See more on this page.
Reaching Out
If you are interested in the opportunities mentioned above, please email me with [phd-appl] tag in the subject line and attach the following:
- CV
- Brief statement of interest in the email body
- Research proposal
- Any additional materials that support your application (recommendations, transcripts, etc.)
Applications that do not follow the instructions, look too generic, or otherwise not serious will be ignored.
Postdoc Opportunities
I do not have any funded postdoc openings at the moment. However, I am always open to discussing potential collaborations.
Also, you are most welcome to propose a collaboration based on an externally-funded postdoctoral fellowship such as, for example:
- Marie Skłodowska-Curie Postdoctoral Fellowship
- 1851 Research Fellowship
- EPSRC Post-Doctoral Fellowship [outdated link]
Reaching Out
To discuss potential collaborations, please email me with [postdoc-appl] tag in the subject line.