PhD studentships in “Efficient and Reliable Probabilistic Machine Learning”

Deadline: 12 March 2024 (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 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 funded studentships starting in the academic year 2024/25 cover:

  • 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 £1,000 per year.

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 2024.

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

Complete applications submitted by 12 March 2024 will receive full consideration; after that date applications will be considered until the position is filled. The start date is September 2024 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.

The PhD Programme
The PhD will be part of the wider PhD Programme offered by the Faculty of Law. The PhD programme is a three-year research programme aimed at training PhD students to undertake in depth research at an international level, hone their teaching and communication skills to pursue an academic career, as well as acquire transferrable skills for undertaking a career in the public or private sector.

During the three-year period, in accordance with section 7, subsection 2 of the Ministerial Order on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions, the PhD student must attend courses, acquire teaching experience and undertake research stays at other research institutions and universities in Denmark and abroad.

The most important aspect of the PhD programme is the preparation of a written thesis, which following an oral public defence, forms the basis for the award of the PhD degree.

For further information about the PhD programme, please see: The PhD Programme – Faculty of Law – University of Copenhagen (ku.dk).

The Faculty of Law offers supervision by highly qualified academics and provides an excellent opportunity to research contemporary legal issues in an intellectually stimulating environment.

Qualifications

  • Applicants must have obtained a degree that corresponds to the Danish Master of Laws degree which is a second cycle 120 ECTS degree (two years of full-time studies), that builds on a 180 ECTS bachelor’s degree (three years of full-time studies) or equivalent qualifications. Applicants with a non-Danish Master’s degree will have their degree assessed by the Ministry of Higher Education and Science in order to establish, if the Master’s degree is equivalent to a Danish Master’s degree.
  • Applicants must have obtained a minimum overall grade average of 8.2 or above at the Master’s level in accordance with the Danish grading scale (for Danish scale, see https://ufm.dk/en/education/the-danish-education-system/grading-system?set_language=en&cl=en).
  • Applicants may submit their application before they have completed their Master’s Degree. The Master’s degree must be obtained before the start date.
  • Applicants must document an aptitude for research through the meritorious assessment of their final thesis, publications or academic recommendations in order to show that they are capable of undertaking the demanding task of writing a PhD thesis.
  • Applicants must have excellent language skills in English and have excellent communications skills. Applicants must be able to teach at an academic level in Danish or English and to follow PhD courses in English.

Application procedure

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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