PhD Position in AI-driven Reconstruction of Gene Regulatory Networks in the Human Brain

Your role

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.

Project background

How do genetic variants reshape gene regulatory networks in specific human brain cell types? Why do some GWAS variants propagate through molecular systems to affect entire biological pathways, while others appear to have only local effects? And can we reconstruct these causal regulatory relationships directly from large-scale human genetic and single-cell data?

The Department of Genetics at UMCG is seeking a highly motivated PhD candidate to join a newly funded project focused on AI-driven inference of directed gene regulatory networks in the human brain. For many years, human genetics has focused primarily on identifying associations between genetic variants and nearby gene expression changes. However, the central challenge remains: how do these local effects propagate through regulatory networks to ultimately influence cellular function and disease?

Recent advances in large-scale genetics and single-cell functional genomics now make it possible to move beyond correlation-based analyses. Building on strategies developed in eQTLGen phase 2 (Wamerdam et al., 2026), we aim to reconstruct directed gene regulatory networks across brain cell types, linking GWAS variants to downstream molecular effects and biological pathways. In this project, you will develop and apply computational and statistical methods to infer causal regulatory structure from population-scale genetic data. A key goal is to connect disease-associated variants, particularly in neurodegenerative disorders, to specific cell types and regulatory pathways in the brain.

In addition, we will leverage newly available single-cell multi-ome datasets from human brain (~1,000 samples), enabling joint modeling of chromatin state and gene expression within individual cells. These data provide a unique opportunity to infer regulatory relationships directly from paired molecular modalities and to study gene regulation at unprecedented resolution. The project builds on prior work in blood, where we have developed and applied causal inference approaches for large-scale regulatory mapping. The current project extends these ideas to the human brain, where cell-type specificity and disease relevance are substantially higher. The PhD candidate will be embedded in the Franke group, an internationally leading group in functional genomics, eQTL mapping, and large-scale human genetic analysis, and will collaborate with international consortia in neurogenomics and systems biology.

Your profile

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.

What do we offer

• 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.

Links
Functional Genomics
Genetics

Applications should include:

• CV.
• Motivation letter.
• Optional GitHub or technical project examples.
• Contact information for referees.

For questions about the position

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