Postdoc in AI for Knowledge Graphs in Genomics Part-time

Postdoctoral Position Available

We are looking for a highly motivated and dynamic postdoc for a 3-year position, to commence 15th April 2026, or soon thereafter (flexible start date).

The PhD project is part of the NNF-funded research initiative “A-SOuRCCE: AI for Single-cell Omics and Reproducible Cardiometabolic and Cancer Exploration” awarded to Prof. Fran Supek. This ambitious project aims to build autonomous “AI co-pilots” that can navigate complex single-cell datasets to generate and prioritize novel mechanistic hypotheses for human disease.

Our group and research.

The Supek group is an interdisciplinary team at the Biotech Research & Innovation Centre (BRIC), working at the intersection of genomics, molecular biology, and artificial intelligence. The lab performs statistical analysis of large-scale datasets (cancer genomics, population genomics) using cutting-edge techniques including machine learning and genomic language models. We further generate our own genomics data and work with gene editing to generate models of cancer evolution.

We focus on frontier research projects, including the ERC Consolidator project “STRUCTOMATIC”, the Danish Cancer Society project “AI-DRIVERS”, EU Horizon consortia “DECIDER” and “LUCIA” and others. We are embedded in a broad network of international collaborators and offer a vibrant, international research environment.

The lab is at the Biotech Research & Innovation Centre (BRIC), a flagship Danish biomedical research institute, and a part of the University of Copenhagen, a highly-ranking European university. More information about the group is given on the lab website.

The Project “A-SOuRCCE”.

Modern single-cell sequencing can map the activity of thousands of genes in individual cells, but interpreting this vast data remains a bottleneck. A-SOuRCCE aims to solve this by developing an Agentic AI framework — a system where Large Language Models (LLMs) act as reasoning engines. However, an AI agent is only as smart as the knowledge it can access.

Your job.

You will act as the Knowledge Graphs in Genomics architect. While the AI agent “reasons” to infer new gene regulatory circuits, you will build the system that grounds that reasoning in established biological fact (e.g. known enhancer-promoter interactions) and in human population genetics (GWAS, QTLs).

You will lead the design and implementation (querying, extension) of the Federated Disease Knowledge Graph (FDKG), a dynamic ecosystem that connects our AI agent to the existing biological knowledge on gene regulation in cancer and in cardiovascular/metabolic disease.

Your responsibilities are anticipated to include:

• Design a federated KG system that allows our AI to query knowledge graphs across diverse resources — from enhancer-to-gene links (e.g. inferred from Hi-C, from CRISPRi experiments) to pathways (e.g. Reactome) and literature-derived cell state signatures — without creating a monolithic database.
• You will implement workflows (e.g., using LLMs as “readers” of scientific papers) to continuously extend the knowledge graph with new findings from literature, and also our own analyses performed on single-cell datasets by others on the project.
• Implement the “Impact Score” assessment for novel findings (population genomics integration): A core innovation of A-SOuRCCE is validating single-cell findings using population genetics. You will develop a module that automatically checks if regulatory elements (enhancers) identified in single-cell data are enriched for disease-risk variants (GWAS hits, eQTLs). This links cellular mechanisms directly to human disease risk.

Team collaboration.

You will work closely with the other postdoc/engineer to ensure the federated Knowledge Graph is query-ready for the AI agent, and with PhD students who will apply your KG system to assess and validated their single-cell data analysis, which they applied to cancer, and to cardiovascular/metabolic diseases.

Profile and qualifications.

We are looking for a highly motivated and enthusiastic scientist with the following competencies and experience:

Essential experience and skills:

• You have a PhD in machine learning, applied math, data science, genomics or epigenomics or transcriptomics, evolutionary biology, (bio)statistics, or related fields.
• You are experienced in computational biology tools/databases, in statistical data analysis and visualization, and proficient in writing code in at least one programming language.
• You have an active interest in the biology of gene regulation, and its role in cancer and cardiovascular disease, and in learning new bioinformatics and artificial intelligence approaches.
• Solid level of written and spoken English
• At least one high-impact first-author publication or preprint from your PhD and/or MSc work.

Desirable experience and skills:

• Experience with biological networks, particularly gene regulation networks however also protein-protein interactions, or genetics interactions, or similar.
• Experience with knowledge graphs/ontologies in specific, or more generally with tools that deal with graphs and graph databases (e.g., Neo4j, RDF, SPARQL, Cytoscape…).
• Experience with human genetics (GWAS, eQTL analysis, LD-score regression) or functional genomics (ChIP-seq/ATAC-seq integration).
• Experience/interest in Large Language Models (LLMs) and their use in knowledge extraction.
• Keen interest in programming, algorithm development, mathematics, complexity, and puzzles.
• Two or more first-author publications and/or preprints and/or high-ranking conference proceedings (incl. shared first authorship) from your PhD and/or MSc work.

Place of employment

The place of employment is at the BRIC, Faculty of Health and Medical Sciences, University of Copenhagen.

We offer Integration into a dynamic, interdisciplinary team of biologists and data scientists. BRIC is an elite scientific institution, providing creative and stimulating working conditions in dynamic and international research environment. Our research facilities include modern laboratories and high-performance computing facilities. In addition to a competitive salary and social benefits, you will have the opportunity to live in Copenhagen, consistently ranked as one of the world's most livable cities.

Terms of employment

The average weekly working hours are 37 hours per week.

The position is a fixed-term position limited to a period of 3 years. The starting date is 15th April 2026, or as soon as possible thereafter.

Salary, pension and other conditions of employment are set in accordance with the Agreement between the Ministry of Finance and AC (Danish Confederation of Professional Associations) or other relevant organisation. Currently, the monthly salary starts at 39,237 DKK/approx. 5,252 EUR (November 2025 level). Depending on qualifications, a supplement may be negotiated. The employer will pay an additional 18,07 % to your pension fund.

Foreign and Danish applicants may be eligible for tax reductions, if they hold a PhD degree and have not lived in Denmark the last 10 years.

The position is covered by the Job Structure for Academic Staff at Universities 2025.

Questions

For informal inquiries about the project and the postdoc position, please contact Prof. Fran Supek; fran.supek@bric.ku.dk

Foreign applicants may find this link useful: www.ism.ku.dk (International Staff Mobility office).

Application procedure

Your online application must be submitted in English by clicking 'Apply now' below. Furthermore, your application must include the following documents/attachments – all in PDF format:

1. Motivated letter of application (max. one page).
2. CV incl. education, work/research experience, language skills and other skills relevant for the position.
3. A certified/signed copy of a) PhD certificate and b) Master of Science certificate. If the PhD is not completed, a written statement from the supervisor will do.
4. List of publications (including preprints, high-ranking conference proceedings, and code repository). Please include summaries for 1-3 selected publications, describing the significance and main results from the study, and specifying your contributions to each particular study.

Deadline for applications: 10 February 2026, 23.59pm CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the above