The University of Helsinki is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world.
The Department of Computer Science, which is part of the Faculty of Science, is a leading Computer Science research and teaching unit in Finland. The research themes of the Department cover machine learning and algorithms, computer networks and distributed systems, software systems and bioinformatics.
The Department of Computer Science, Faculty of Sciences, University of Helsinki invites applications for a
Doctoral Student in Sample-Efficient Probabilistic Machine Learning
Position description
The Machine and Human Intelligence research group led by Assistant Professor Luigi Acerbi is looking for a PhD candidate eager to work on new machine learning methods for smart, robust, sample-efficient probabilistic inference, with applications in scientific modeling — such as, but not limited to, computational and cognitive neuroscience. The candidate will join a newly established research group with strong links to the Finnish Center for Artificial Intelligence (FCAI).
In our group, we are interested in developing novel machine learning approaches for building approximate Bayesian posteriors using only a small number of likelihood evaluations, which can be a game-changer for complex models or when resources are limited. Think of Bayesian optimization, a very effective technique to optimize black-box functions, but scaled up to perform full Bayesian inference. A state-of-the-art framework being developed in our group is Variational Bayesian Monte Carlo (VBMC), which combines Gaussian process surrogates, active learning, variational inference and Bayesian quadrature (Acerbi, NeurIPS; 2018, 2020).
Promising thesis projects include extending the representational power of VBMC (e.g., discrete variables, more complex posteriors, higher dimension); exploiting recent advances in Gaussian process inference for superior scalability; combining VBMC with Bayesian deep learning; strengthening the connections with simulator-based inference; and exploring the theoretical properties of the framework.
The position is full-time, funded for four years and will be filled as soon as possible, with a negotiable starting date in 2021.
Requirements
The work involves research-related activities, including conducting theoretical and applied research, designing and programming machine learning software, computational and data analyses, writing research articles, participating in and presenting research at academic conferences, and teaching-related activities. You may also have the chance to do short stays abroad to work with collaborators.
The ideal candidate has a strong background in computational statistics and/or machine learning, and experience with Bayesian methods (e.g., MCMC, variational inference, probabilistic programming). The following constitute an advantage, but are not required:
- Experience in implementing efficient code in Python (e.g., Jax).
- Familiarity with Gaussian processes, Bayesian optimization.
- Experience with computational modeling and statistical data analysis (e.g., in an applied field such as computational neuroscience).
- Previous research experience and publications.
Excellent written and oral communication skills in English are needed.
Applicants should have a MSc in computer science, data science, applied mathematics and statistics, physics, computational neuroscience, or a related field.
Applicants who do not currently hold a doctoral student status in the Doctoral Programme in Computer Science at the University of Helsinki are eligible to apply, but in the event of hiring, they are expected to acquire the status during the standard 6-month probationary period.
Salary and benefits
The position is full-time and funded for four years. For a doctoral student, the starting salary is 2350–2700 euros/month, depending on previous qualifications and experience.
The University of Helsinki offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The International Staff Services office assists employees from abroad with their transition to work and live in Finland.
How to apply
Please submit your application with its attachments to the group leader, Luigi Acerbi (please find email address below), specifying "Application for PhD in Sample-Efficient Probabilistic Machine Learning" as the email subject.
The application should include the following attachments as pdf files (in English):
- CV with possible publications and previous project
- A copy of your transcripts (list of courses completed during BSc/MSc and grades)
- Cover letter (1-page) with motivation, research interests and match to the project
- Contact details of two referees who could provide a letter of recommendation (can be included at the end of the CV)
Applications will be considered until the position is filled.
Additional information
For more information on the group and the position, please:
- visit the research group page
- contact the group leader Luigi Acerbi (luigi.acerbi@helsinki.fi)
Please visit the university website for more information on the Doctoral Programme in Computer Science (DoCS).
For more information on the eligibility and required educational documents to doctoral studies at the University of Helsinki, please visit this page.
And these are some good reasons to move to Finland.
Our group is committed to principles of diversity, equality and inclusion. We encourage applications from women, racial and ethnic minorities, and other under-represented individuals.