3-years PhD position in probabilistic machine learning and statistics (ref 307053)

Unist; Oslo; Hybrid; NOK 480,000 - 520,000; Full time

Job summary

The University of Oslo invites applications for a PhD First Stage Researcher (R1) to develop probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging.

The project aims to deliver a scalable, interpretable framework for probabilistic unsupervised learning in biology, addressing data scale, heterogeneity, and complex dependencies across space and function.

Responsibilities

• Develop probabilistic latent-variable methods for large structured biological data.
• Apply methods to genomics, spatial transcriptomics, and fluorescence imaging.
• Address high dimensionality and heterogeneity in biomedical data.
• Contribute to an interpretable framework for probabilistic unsupervised learning in biology.

Job description

Organisation/Company University of Oslo Research Field Medical sciences Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Oct 2026 - 23:00 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The project focuses on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging.

High-dimensional and structured biological data are increasingly common in modern biomedical research but remain challenging to analyse because of their scale, heterogeneity, and complex spatial and functional dependencies. Existing methods often rely on restrictive assumptions or application-specific computational workflows. The PhD project aims to address these limitations by developing a unified, scalable, and interpretable framework for probabilistic unsupervised learning for structured biological data.