Doctoral Student in AI-driven Phylogenetic Analysis of Cancer Evolution

About the role

Four-year fully funded PhD at KTH on AI-driven phylogenetic analysis of cancer evolution. The project sits at the intersection of computational biology, machine learning, and oncology, reconstructing tumour evolutionary trees from genomic sequencing data and using them to predict resistance and progression. Deadline 3 June 2026.

Responsibilities

• Develop probabilistic and deep-learning methods for inferring cancer phylogenies from single-cell and bulk sequencing data.
• Validate methods on real patient cohorts in collaboration with clinical partners at Karolinska Institutet.
• Publish in venues such as Nature Methods, Genome Research, NeurIPS, and Cell Systems.
• Complete coursework in machine learning, statistical genetics, and computational biology.

Requirements

• Master’s degree in computer science, computational biology, mathematics, statistics, or a related field.
• Solid grounding in probability and statistics.
• Python programming; experience with at least one deep learning framework.
• Fluent English.

Nice to have

• Background in phylogenetics, population genetics, or cancer genomics.
• Experience handling next-generation sequencing data.
• Publications or preprints in machine learning or computational biology.

How to apply

Search “Phylogenetic Analysis of Cancer Evolution” on KTH’s Varbi portal at the apply link. Submit CV, transcripts, research statement, Master’s thesis, and two references via Varbi before 3 June 2026.