PhD Studentship in “Efficient and Reliable Probabilistic Machine Learning”

Deadline: 31 March 2025 (or until the position is filled) 

One fully funded PhD positions to work with Dr Antonio Vergari in the School of Informatics at the University of Edinburgh, on a project in the research areas of Probabilistic Machine Learning, Neuro-Symbolic AI and Deep Generative Models. 

The PhD candidate will research the methodological foundations for a new generation of probabilistic models and programs that come with guarantees while being efficient. The project will expand the theoretical boundaries of reliable probabilistic inference and unifying modern probabilistic formalisms to design modular algorithms for complex probabilistic inference with guarantees in the presence of constraints and heterogeneous data. This will require devising novel and efficient algorithms to learn probabilistic models and programs from data, and complex reasoning with deep generative models. 

Candidate’s profile 

• A strong background in math, statistics probability and programming, as demonstrated by grades in relevant courses or by previously taken projects.
• Proficiency with modern deep learning frameworks such as pytorch, jax
• A Bachelor’s Hons degree (classification 2.1 or above, or equivalent) and/or Master’s degree in Computer Science, Mathematics, Physics or Engineering.
• Proficiency in English (both oral and written)
• Previous experience in probabilistic modeling and programming 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 (£4,786* per annum) or overseas fee status (£33,100 per annum)
• A tax-free stipend of GBP £19,237 per year for 3.5 years
• Additional programme costs of £1000 per year.

*Rates are for 24/25 as 25/26 rates not yet confirmed 

Application Information 

Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology with a start date of September 2025. 

Applicants should state “Efficient and reliable probabilistic machine learning” and the research supervisor (Dr Antonio Vergari) in their application. 

Complete applications submitted by 31 March 2025 will receive full consideration; after that date applications will be considered until the position is filled. The start date is September 2025 but other start dates can be considered. 

Applicants must submit: 

• All degree transcripts and certificates (and certified translations if applicable).
• Evidence of English Language capability.
• A short research proposal highlighting how previous experience and current interests match this position (max 2 pages). This is perhaps the most crucial document, take your time to carefully write it!
• A full CV and cover letter (max 1 page).
• Two references (it is the applicant’s responsibility to ensure reference letters are received before the deadline). 

Only complete applications will be considered. 

Environment 

The School of Informatics is one of the largest in Europe and currently the top Informatics institute in the UK for research power, with 40% of its research outputs considered world-leading (top grade). The University of Edinburgh is constantly ranked among the world’s top universities and is a highly international environment with several centres of excellence. 

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