PhD position in AI-driven Discovery of New Cancer Genes

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.

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.

- A fully funded 4-year PhD position at UMCG
- Your salary is € 3.108,- gross per month in the first year an d up to a maximum of € 3.939 gross per month in the last fourth year (scale PhD of 1st of July 2025). Additionally, the UMCG offers an 8% holiday allowance, an 8.3% year-end bonus. The conditions of employment comply with the Collective Labour Agreement for University Medical Centres (CAO-UMC). the CAO-UMC.
- 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

Application process:
For us it would be very valuable if you can provide the following types of information. This will help us strongly in our selection process.
1. Curriculum Vitae (max. 2–3 pages):
Education and relevant coursework
Research experience and/or internships
Technical skills (programming languages, ML frameworks, genomics experience)
Publications, preprints, or software contributions (if applicable)

2. Motivation letter (max. 1 page):
The motivation letter should explicitly address the following points:
Why you are interested in this PhD position and in applying AI to cancer
Your prior experience with machine learning, data analysis, or computational research
Which aspects of the project you expect to be able to work on independently at the start of the PhD
What you hope to learn during the PhD

3. Evidence of technical experience:
Please include at least one of the following:
A link to a GitHub/GitLab/Bitbucket repository
A short technical report, preprint, or undergraduate thesis chapter
Code or supplementary material associated with a publication or project
If code is not public, applicants may briefly describe their contribution and tools used.

4. Names and contact details of at least two referees:
Referees should be:
Direct supervisors (e.g. MSc thesis supervisor, internship mentor)
Able to comment on the applicant’s technical skills, independence, and collaboration style

Recommendation letters are not required at the application stage but may be requested later.

Applications that do not explicitly address all points listed above will not be considered. Shortlisted candidates may be asked to complete a brief technical screening exercise prior to the interview.

More information on our research group can be found on www.functionalgenomics.org

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 (https://functionalgenomics.org), an internationally leading group in functional genomics and eQTL analysis, and will collaborate closely with partners at NKI, Prinses Maxima Centre, and international consortia

Lude Franke
l.h.franke@umcg.nl