As a PhD student, you will:
- develop and apply methods to infer directed gene regulatory networks from large-scale human genetic data
- link GWAS variants to downstream molecular pathways through cell-type-specific regulatory models;
- perform large-scale cis-eQTL and trans-eQTL analyses in brain datasets;
- integrate single-cell RNA-seq and single-cell multi-ome data (~1,000 samples) to infer regulatory structure;
- extend and adapt existing causal inference frameworks developed in blood to brain tissue;
- explore how regulatory effects differ across brain cell types and neurodegenerative disease contexts;
- 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 statistical genetics, machine learning, and functional genomics, and work in a highly collaborative and interdisciplinary environment.
We are looking for a candidate who:
Required:
- holds (or will soon obtain) a Master’s degree in Bioinformatics, Computational Biology, Artificial Intelligence, Data Science, Computer Science, Statistics, Mathematics, Physics, or a related field;
- has strong programming skills in Python and experience with data analysis or machine learning pipelines;
- is motivated to work at the interface of AI, genetics, and systems biology;
- enjoys working with large-scale, open-ended computational research problems;
- has good written and spoken English;
Nice to have:
- experience with statistical genetics (eQTLs, GWAS, gene regulation);
- familiarity with machine learning or deep learning methods;
- experience with single-cell or multi-omics data;
- experience working on HPC systems or cloud computing;
- interest in neuroscience or neurodegenerative disease biology;
- prior experience in an international research environment;
Prior genomics experience is helpful but not required for strong quantitative candidates.
This position is particularly suited for candidates who enjoy combining AI, statistical modeling, and biological interpretation to reconstruct complex systems from large-scale data.
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.
- Salary and employment conditions according to the CAO UMC, which include a 36 hour work week, starting salary of € 3.217, increasing yearly to € 4.077 in the last year.
- 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 Medical Centres (CAO-UMC).
- Access to world-class human brain genomics datasets and high-performance computing infrastructure.
- Training in statistical genetics, machine learning, and functional genomics.
- Close collaboration with international consortia and leading research groups.
- Opportunities for conferences, training, and international networking.
- A stimulating, collegial, and inclusive research environment.
- Strong track record of PhD graduates moving into top academic and industry positions in data-driven biology and AI.
Please use the the digital application form at the bottom of this page - only these will be processed. You can apply until
15 July 2026. Within half an hour after sending the digital application form you will receive an email- confirmation with further information.
