University of Oslo
4 days ago
PhD in Probabilistic Machine Learning and Statistics for Biological Data University of Oslo in Norway
Degree Level
PhD
Field of study
Computer Science
Funding
3-year PhD position. A 3–6 month research stay at Columbia University is possible. No stipend amount or tuition details are stated.
Deadline
Oct 4, 2026
Country
Norway
University
University of Oslo

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Apply for this position
Keywords
Suggested positions
About this position
PhD opening at the University of Oslo (Oslo Centre for Biostatistics and Epidemiology) with Prof. Valeria Vitelli.
The project focuses on probabilistic machine learning, statistics, and factor models for high-dimensional structured biological data. Research applications include spatial transcriptomics and fluorescence imaging, with involvement in the FunGen-AD Alzheimer’s consortium.
Eligibility highlights: MSc in Statistics, ML, Maths, CS, or Physics; strong academic performance; and experience with Python, PyTorch/JAX, and Bayesian/probabilistic ML. The position starts ASAP and is based in Oslo, Norway.
Funding details are not specified in the post, but the opportunity is a 3-year PhD position. A 3–6 month research stay at Columbia University may be possible.
Deadline: 4 October 2026. Apply via the official job posting linked in the post.
Funding details
3-year PhD position. A 3–6 month research stay at Columbia University is possible. No stipend amount or tuition details are stated.
What's required
MSc in Statistics, Machine Learning, Mathematics, Computer Science, or Physics; strong grades (Bachelor C+ / Master B+); background in Python, PyTorch/JAX, and Bayesian/probabilistic machine learning.
How to apply
Apply through the linked University of Oslo / Jobbnorge posting. Review the full job ad, then submit the application materials before the deadline.
More information can be found here
Ask ApplyKite AI

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.