PhD Studentship in Geometric Learning and Uncertainty Quantification

Deadline: 7th January 2026

One fully funded, full-time PhD position to work with Dr. Viacheslav Borovitskiy in his new research group at the School of Informatics, University of Edinburgh.

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:

• Geometric learning: exploiting the structure of data (e.g., symmetries), learning on structured domains (e.g., graphs, manifolds)
  (to achieve data efficiency and respect constraints)
• Uncertainty Quantification: building models that quantify uncertainty associated with their own predictions
  (to support model-based decision-making and safety-critical applications)

Candidate’s profile 

• A good Bachelors degree (2.1 or above or international equivalent) and/or Masters degree in a relevant subject (computer science, mathematics, or related subject)
• Proficiency in English (both oral and written)
• Python programming skills.
• Solid mathematical background is desirable. 

Studentship and eligibility 

The School funded studentship starting in the academic year 2025/26 covers: 

• Full time PhD tuition fees for a student with a Home fee status (£5,006 per annum* 25/26) or overseas fee status (£33,100 per annum in 25/26).
• A tax-free stipend at UKRI rate (£20,780 for 25/26, subject to annual increase for 3.5 years). 

Application Information 

Important. Interested applicants are highly encouraged to file an informal application first, by following the instructions on https://vab.im/vacancies/. This serves to check if there is a mutual interest with minimal bureaucratic effort on both sides. 

Formal applications should go through the University’s admissions portal (EUCLID), the applicants should choose the “Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology” programme (https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2025&id=489) with a start date of 14 September 2026 (a starting date of 1 May 2026 can also be considered).

In the formal application, applicants should state “PhD studentship in Geometric Learning and Uncertainty Quantification” as project title and the research supervisor (Dr. Viacheslav Borovitskiy) in their application and Research Proposal document.

Complete applications submitted by Wednesday 7 January 2026 at 23:59pm will receive full consideration; after that date applications will be considered until the position is filled. The anticipated start date is May/September 2026 but later start dates might need to be considered for international applicants needing to complete immigration processes prior to commencing studies.

Applicants must submit: 

• All degree transcripts and certificates (and certified translations if applicable)
• Evidence of English Language capability (where applicable).
• A short research proposal (max 2 pages)
• A full CV and cover letter describing your background, suitability for the PhD, and research interests (max 2 pages).
• Two references (note that it the applicant’s responsibility to ensure reference letters are received when necessary). 

Only complete applications (i.e., those that are not missing the above documentation) will progress forward to Academic Selectors for further consideration. 

Environment 

You will be a part of the Institute for Adaptive and Neural Computation, a vibrant community of researchers in machine learning and related areas. 

Apply: https://informatics.ed.ac.uk/phd-studentship-in-geometric-learning-and-uncertainty-quantification

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