PhD positions in Probabilistic Machine Learning at the University of Edinburgh
Antonio Vergari
Two fully funded PhD positions to work with Dr Antonio Vergari in the School of Informatics at the University of Edinburgh, on projects in the research areas of Probabilistic Machine Learning, Neuro-Symbolic AI and Deep Generative Models.
PhD candidates will research the methodological foundations for a new generation of probabilistic models and programs that come with guarantees while being efficient. Possible topics include but are not limited to: i) expanding the theoretical boundaries of reliable probabilistic inference and unifying modern probabilistic formalisms ii) designing modular algorithms for complex probabilistic inference with guarantees in the presence of constraints and heterogeneous data iii) devising novel and efficient algorithms to learn probabilistic models and programs from data, iv) 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 studentship covers:
- Full time PhD tuition fees for a student with a Home fee status (£4,712 per annum) or overseas fee status (£29,700 per annum)
- A tax-free stipend of GBP £18,622 per year for 3.5 years
- Additional programme costs of £1000 per year.