PhD fellowship in AI for Single-Cell Disease Genomics at the Supek Group, BRIC Full-time

PhD Fellowship In Ai For Single-Cell Disease Genomics

We are offering a PhD fellowship in AI for Single-Cell Disease Genomics commencing 1 May 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 A-SOuRCCE project

Modern single-cell sequencing can map the activity of thousands of genes in individual tumor cells, but interpreting this vast amount of data remains a critical bottleneck. A-SOuRCCE aims to solve this by developing an agentic AI framework — a system where Large Language Models (LLMs) act as reasoning engines to plan analysis, query knowledge graphs, and discover biological mechanisms. The goal is to move beyond simple data description to the automated discovery of causal drivers of disease.

Your PhD work

Your studies will be centered on cancer mechanisms research, in particular transcriptional landscapes analysis to elucidate cell type and state, and epigenomic analysis to assess effects of chromatin remodelling, and of copy number alterations on gene expression. You will work closely with a team of students and postdocs to build the “eyes” of the AI agent, enabling it to “see” and interpret tumor heterogeneity.

Your specific responsibilities and research topics will include:

• You will lead the development of the standardized analysis workflows for scRNA-seq and scATAC-seq data, inferring cell types and states in a heterogeneous tissue sample, and inferring regulatory circuits, in particular enhancer-to-gene links.
• Curating and processing a diverse atlas of public single-cell tumor datasets (e.g., Glioblastoma, Neuroblastoma, Lung Cancer) and/or public single-cell datasets of cardiovascular/metabolic disease, to serve as the training ground for the AI agent.
• You will apply the full A-SOuRCCE agent to these cancer datasets to infer gene regulatory circuits involving gene expression and differential use of enhancers, and later to validate its “reasoning.” You will assess whether the AI can autonomously recapitulate known cancer biology (e.g., specific cell cycle programs, or oncogene-induced changes, or immune evasion mechanisms) and use it to propose novel hypotheses.
• You will work in a team with colleagues, including students/postdocs (focussed on gene regulatory networks, covering sc datasets in various diseases, and on design of the AI agent for knowledge extraction and validation) and software engineers to integrate modules into the central AI agent.

Profile and qualifications

We are looking for a highly motivated and ambitious PhD candidate, ideally with a background in computational biology, bioinformatics, data science, or a related field.

• Desirable: Solid programming skills in Python and/or R. Familiarity with workflow management systems (e.g., Nextflow, Snakemake) or machine learning frameworks (e.g. PyTorch).
• Desirable: Experience with analyzing omics data, preferably single-cell RNA-seq or ATAC-seq.
• Desirable: Understanding of cancer biology, specifically somatic evolution or tumor heterogeneity.
• Desirable: Curiosity about Large Language Models (LLMs) and their application to science.

Questions

For informal inquiries about the project and the PhD student 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).

Principal supervisor is Prof. Fran Supek, BRIC, fran.supek@bric.ku.dk, +45 3533 3008

Start: 1st May 2026, or soon thereafter (with flexibility)

Duration: 3 years as a PhD student, with possible extension

Job description

Your key tasks as a PhD student at SUND are:

• As a central task, carrying through an independent research project in a cutting-edge scientific topic, with supervision.
• Completing PhD courses or other equivalent education corresponding to approximately 30 ECTS points.
• Participating in active research environments, including integration into the hosting lab, and a short stay at another research team.
• Obtaining experience with teaching or other types of dissemination related to your PhD project
• Writing a PhD thesis on the grounds of your project

Key criteria for the assessment of applicants

Applicants must have qualifications corresponding to a master's degree related to the subject area of the project, e.g. bioinformatics/genomics, mathematics, data science, physics, computer science or molecular biology. Please note that your master's degree must be equivalent to a Danish master's degree (two years).

Other important criteria are:

• The grade point average achieved
• Professional qualifications relevant to the PhD project
• Previous publications (incl. papers, preprints, conference proceedings)
• Relevant work experience and other professional activities
• Motivation and a curiosity driven mind-set, with an interest in genomics and/or AI
• English language skills

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 start date is 1st May 2026, or later thereafter, as agreed.

The employment is conditioned upon the applicant's successful enrolment as a PhD student at the Graduate School at the Faculty of Health and Medical Sciences, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

The PhD study must be completed in accordance with The Ministerial Order on the PhD programme (2013) and the Faculty's rules on achieving the degree.

Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. Depending on seniority, the monthly salary starts at approximately 31,242 DKK/Roughly 4,182 EUR (November 2025 level) plus pension.

Questions

For specific information about the PhD fellowship, please contact the principal supervisor.

General information about PhD studies at the Faculty of Health and Medical Sciences is available at the Graduate School's website: https://healthsciences.ku.dk/phd/guidelines/

Application procedure

Your application must be submitted electronically by clicking 'Apply now' below. The application must include the following documents in PDF format:

1. Motivated letter of application (one page