PhD studentships in “Efficient and Reliable Probabilistic Machine Learning”
Deadline: 31 May 2022 (or until the position is filled)
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 area of “Efficient and reliable probabilistic machine learning”.
There is a desperate need in machine learning (ML) nowadays for ensuring that systems we deploy are reliable and behave according to our expectations. Equally important, we need automatic ways for learning new systems that are provably reliable by design. PhD candidates will research the methodological foundations for a new generation of probabilistic models that come with guarantees while being efficient. As such, this research will be at the intersection of modern automated reasoning, deep probabilistic learning and generative modelling. These positions come with exceptional freedom in terms of research topics and are not tied to any specific project. Possible topics include but are not limited to: i) expanding the theoretical boundaries of reliable probabilistic inference  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) unifying and connecting modern probabilistic formalisms , v) combining deep learning, complex reasoning and causality.
- A strong background in discrete and continuous math, statistics probability and programming, as demonstrated by grades in relevant courses or by previously taken projects and internships.
- A Bachelor’s Hons degree (classification 2.1 or above, or the international equivalent) and/or Master’s degree in Computer Science, Mathematics, Physics or Engineering.
- Proficiency in English (both oral and written).
Studentship and eligibility
The studentship starting in the academic year 2022/23 covers:
- Full time PhD tuition fees for a student with Home fee status (£4,596 per annum) or overseas fee status (£28,000 per annum)
- A tax free stipend of GBP £16,062 per year for 3.5 years.
Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: PhD Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology - 3 Years (Full-time).
Applicants who have a home fee status should apply for 01 October 2022 start date.
Applicants who have an international fee status and who require a student visa to study should apply for 01 January 2023 start date.
Applicants should state “Efficient and reliable probabilistic machine learning” and the research supervisor (Dr Antonio Vergari) in their application and Research Proposal document.
Complete applications submitted by 31 May 2022 will receive full consideration; after that date applications will be considered until the position is filled.
Applicants must submit:
- All degree transcripts and certificates (and certified translations if applicable).
- Evidence of English Language capability (where applicable).
- A short research proposal highlighting how previous experience and current interests match this position (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 before the deadline).
Only complete applications (i.e. those that are not missing the above documentation) will progress forward to the supervisor and academic selector for further consideration.
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), and almost 50% considered top grade for societal impact. The University of Edinburgh is constantly ranked among the world’s top universities and is a highly international environment with several centres of excellence.