General

Please visit the group page to learn about our research directions, current members, and alumni.

Reaching Out

If you are interested in the opportunities below, please email me with [group-appl] tag in the subject line and attach the following:

  • CV
  • Brief statement of interest in the email body, plus where you found the opportunity
  • Research proposal
  • Any additional materials that support your application (recommendations, transcripts, etc.)
Please ensure that you meet the eligibility requirements for the program and funding before applying.

It is highly recommended to reach out to me via email first, even if you are applying through ELLIS, CDTs, or scholarships.

Applications that do not follow the instructions, look too generic, or otherwise not serious will be ignored.

Fully-funded PhD in Geometric Learning and/or Uncertainty Quantification

Join my 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.

Application deadline: December 31, 2025, or until the position is filled.
Please note that applications will be reviewed on a rolling basis every 2-4 weeks, so early submission is advantageous.
You can also apply through ELLIS. However, in this case reaching out through email is still recommended.

When: Ideally, starting May or September 2026.

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: 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. Visa/relocation costs are not covered.

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.

I am reopening this position previously advertised last year after the selected candidate was unable to join due to ATAS (visa-related) clearance issues.

Other PhD Opportunities

You can view the details on other PhD opportunities by clicking .

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:

I am also willing to be a secondary advisor for students from the Maxwell Institute Graduate School in Modelling, Analysis & Computation CDT.

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: