PhD position in AI-driven Discovery of New Cancer Genes

The Department of Genetics at UMCG is seeking a highly motivated PhD student to work on a newly funded project focused on identifying functional non-coding somatic mutations in cancer using sequence-based deep learning models.

PhD position in AI-driven Discovery of New Cancer Genes

Job description

As a PhD student, you will:

• Retrain and adapt sequence-based deep learning models (e.g. Borzoi) to prioritize non-coding variants with downstream functional effects
• Integrate AI-based predictions with large-scale cancer whole-genome and (single-cell) RNA-seq datasets
• Identify non-coding regulatory regions enriched for functional somatic mutations across cancer types, with a focus on rare cancers
• Validate predictions using somatic eQTL analyses and gene expression data
• Contribute to open-source software and publish results in peer-reviewed journals
• Present your work at international conferences and consortium meetings

You will receive close supervision and training in both machine learning and functional genomics, and work in a highly collaborative, interdisciplinary environment.

Project background

For decades, cancer genomics has concentrated on somatic mutations in only ~2% of the genome the protein-coding regions, while the vast non-coding majority of the genome was largely inaccessible. We have very recently developed sequence-based AI models that allow us to systematically study this previously ignored 98% of the genome, uncovering many new genes in which somatic promoter mutations play an important role in cancer development.

In this project, you will help improve these state-of-the-art deep learning models (including PARM and Borzoi) to distinguish actionable regulatory mutations—those with downstream molecular consequences—from variants with only local or negligible effects.

The project combines large-scale cancer whole-genome sequencing data with functional genomics and machine learning.

The PhD student will be embedded in the Franke group, an internationally leading group in functional genomics and eQTL analysis, and will collaborate closely with partners at NKI, Prinses Maxima Centre, and international consortia.

What do we need

Required:

• Holds (or will soon obtain) a Master’s degree in Bioinformatics, Artificial Intelligence, Computational Biology, Data Science, or a related field
• Has solid programming skills in Python, and experience developing or modifying analysis pipelines or machine-learning models
• Is motivated to work at the interface of AI and genomics
• Has strong analytical thinking skills and enjoys working with large datasets
• Has good written and spoken English

Nice to have:

• Experience with deep learning models
• Familiarity with genomics concepts (eQTLs, gene regulation, RNA-seq, WGS)
• Experience working on HPC systems or cloud computing
• Interest in cancer biology
• Previous experience in an international research environment and strong references from direct supervisors are considered an advantage.

This position is best suited for candidates who enjoy working independently on open-ended research questions and who are comfortable combining machine learning with biological interpretation. The Franke group values open scientific discussion, frequent interaction, and independence. PhD students are expected to actively present unfinished work, ask questions, and contribute to collaborative problem solving.

What do we offer

• A fully funded 4-year PhD position at UMCG
• Access to world-class genomic datasets and high-performance computing infrastructure
• Supervision by an experienced team with strong international visibility
• Opportunities for training, conferences, and international collaborations
• A stimulating, collegial, and inclusive research environment
• Strong track record of PhD graduates continuing in academia, industry, or data-driven research roles