PhD/postdoc position in Probabilistic Machine Learning for Pharmaceutical R&D

The 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 , 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 Science, University of Helsinki invites applications for a

Doctoral or Postdoctoral Researcher in Probabilistic Machine Learning for Pharmaceutical R&D

Position description

The led by Principal Investigator Luigi Acerbi is looking for a PhD candidate or postdoctoral researcher eager to work on new machine learning methods for smart, robust, efficient probabilistic inference and experimental design, with applications in and pharmaceutical R&D. The candidate will join an established research group at the Department of Computer Science of the University of Helsinki with strong links to the .

Example research directions for this project include developing new methods for multi-fidelity Bayesian optimization with other surrogate models than Gaussian processes (e.g., , ), developing novel amortized inference techniques, and new methods for transfer learning to new domains or over time. The developed techniques will be applied to usage cases in pharmaceutical R&D provided by our industry partners and within .

The project will include collaborations with other groups at the University of Helsinki () and Aalto University (, ), as well as with pharmaceutical companies and with the broader community of machine learning researchers of FCAI.

The position is full-time, funded for two years possibly renewable for another two years, with a target starting date in September 2023 (later dates can be negotiated). This project is funded by .

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, taking part in international research visits, and teaching-related activities.

The ideal candidate has a strong background in computational statistics and/or machine learning, and experience with Bayesian machine learning methods (e.g., Gaussian processes, variational inference, amortized inference, Bayesian deep learning, etc.). We also require experience in implementing efficient code in Python (e.g., PyTorch). The following constitute an advantage, but are not required:

• Experience with Bayesian optimization and/or Bayesian/optimal experimental design.
• Previous research experience and publications (required for a postdoctoral position).

Excellent written and oral communication skills in English are needed.

Applicants should have – or be about to obtain – a MSc (for the PhD position) or a PhD (for the postdoctoral position) in machine learning, computer science, data science, applied mathematics and statistics, physics, or a related field.

Applicants for the PhD position 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 (PhD position) or 2+2 years (postdoc). The starting salary is 2400–2800 euros/month (for a doctoral student) and 3600–4200 euros/month (for a postdoc), 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 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/postdoc in Probabilistic Machine Learning for Pharmaceutical R&D" as the email subject.

The application should include the following attachments as pdf files (in English):

1. CV with possible list of publications and previous projects;
2. A copy of your transcripts (list of courses completed during BSc/MSc and grades);
3. Cover letter (1-2 pages) with motivation, research interests and match to the project;
4. Contact details of two referees who could provide a letter of recommendation (can be included at the end of the CV);
5. An extended sample of your scientific writing (e.g., your Master's or PhD thesis, or a project report; could be a link if available online).

Applications will be considered until the position is filled.

Additional information

For more information on the group and the position, please:

• visit the
• contact the group leader Luigi Acerbi ()

Please visit the university website for more information on the .

For more information on the eligibility and required educational documents to doctoral studies at the University of Helsinki, please visit .

And these are 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.

Appendix: Background references and material

Example probabilistic machine learning methods developed in our group and with collaborators:

• Acerbi (2018). Variational Bayesian Monte Carlo. NeurIPS ().
• Trinh, Heinonen, Acerbi, Kaski S (2022). Tackling covariate shift with node-based Bayesian neural networks. ICML ().
• Li, Clarté & Acerbi (2023). Fast post-process Bayesian inference with Sparse Variational Bayesian Monte Carlo. arXiv preprint ().
• Huang, Haussmann, Remes, John, Clarté, Luck, Kaski & Acerbi (2023). Practical Equivariances via Relational Conditional Neural Processes. arXiv preprint ().

Virtual laboratories (from our collaborators):

• Klami, Damoulas, Engkvist, Rinke & Kaski. Arto Klami, Theodoros Damoulas, Ola Engkvist, Patrick Rinke, Samuel Kaski (2022). Virtual Laboratories: Transforming research with AI. techRxiv preprint ().