DC 11 MSCA Doctoral Network MLCARE (Machine Learning Computational Advancements for peRsonalize[...]

Overview

The University of Copenhagen offers one PhD fellowship for a Doctoral Candidate (DC), starting 1 June 2026 or as soon as possible thereafter. The position is for 36 months and is funded by the MLCARE MSCA Doctoral Network under Horizon Europe’s EU-funded Marie Skłodowska-Curie Actions (MSCA). The successful applicant will join a multidisciplinary international PhD training network and will be expected to attend secondments as well as summer schools and other network training events. The position will be based at the Department of Biology and the Centre for Machine Learning in the Life Sciences (MLLS) at University of Copenhagen (UCPH). We offer creative and stimulating working conditions in a dynamic and international research environment.

Project

Foundation Models for Genomics and Health Trajectories

This project develops self-supervised foundation models that connect genomic variation with longitudinal health data. By building multimodal embeddings from large-scale databases like UK Biobank and GenBank, the PhD researcher will link genetic profiles to disease outcomes, drug response, and risk patterns. Models will be aligned with known biological pathways and benchmarked for clinical relevance—advancing the use of genomic AI in precision medicine.

Second generation DNA foundation models like AlphaGenome and GPNStar provide promising results for scoring disease-causing variants beyond in coding regions. While these models have not been validated or finetuned explicitly on human variation, they can still reason about human variants. Given the growth of whole genome sequencing, there is potential to assess patterns of human variant effects at the population level and develop self-supervised finetuning strategies on human data to improve specificity for reasoning about human disease. Finetuning strategies for human datasets and perturbation datasets focusing on disease will be explored, including temporal aspects of disease progression where possible.

The outcome of this project includes population studies of DNA foundation models and open-source finetuned versions of state-of-the-art DNA foundation models on human data. These components are expected to contribute to future individualized treatment and drug target prediction.

Secondments

• Novo Nordisk (Denmark): disease prediction and drug discovery multi-omics FMs and biological pathways. (3 months, tentatively June–Aug. 2027)
• Pharmatics Limited (UK): incorporating biomedical evidence in omic FMs (3 months, tentatively June – Aug. 2028)
• Fundación Pública Andaluza Progreso Y Salud M.P. (Spain): assessing model generalization over the Andalusian Population Database (3 months, tentatively Dec. 2028 – Feb. 2029)

Supervisors

• Professor Ole Winther - University of Copenhagen
• Marie Lisandra Zepeda Mendoza - Novo Nordisk

Secondment mentors

• Dr. Felix Agakov - Pharmatics Limited
• Dr. Carlos Loucera - Fundación Pública Andaluza Progreso Y Salud

Who are we looking for?

The ideal candidate has a background in machine learning, with an MSc preferably in bioinformatics, computational biology, or a related field. The candidate should be motivated for multidisciplinary research and for developing machine learning models to advance the state of the art in biological sequence analysis.

Essential Qualities

• Curious mindset with a strong interest in the research themes.
• Knowledge and experience in working with protein language models.
• Strong programming skills in Python, with a focus on PyTorch.
• Experience with carrying out a bioinformatics workflow pipeline, including data curation, analysis and modeling.
• Experience with Linux, including high-performance computing environments.
• Strong background in biology, including knowledge of phylogeny and taxonomy.
• Analytical mindset and ability to work systematically.
• Ability to work independently as well as in collaborations.
• Excellent English language skills.
• University-level teaching experience is a plus.

Required Qualifications

To be considered for this position you must have:

• Qualifications corresponding to a Danish master’s degree related to the subject area of the project (bioinformatics, computer science, engineering or similar).
• Fluency in English and excellent communication skills.

Eligibility

Applicants will also be required to meet the European Commission’s MSCA Doctoral Network eligibility criteria, notably:

• Mobility Rule - Applicants can be of any nationality and must not have resided or carried out their main activity in Denmark for more than 12 months in the 36 months immediately before the start of their employment at UCPH. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not considered.
• Doctoral Candidate requirement – must not hold a doctoral degree on the first day of employment. Researchers who have defended but have not yet been awarded are not eligible.
• Doctoral Candidate requirement – must be admitted to a PhD programme leading to a degree in at least one EU Member State or Horizon Europe associated country. Proof of admission must be provided prior to contract start.
• Be working exclusively for the action.

Starting Date

Preferred starting date: 1 June 2026 or as soon as possible thereafter.

Application

The application process is through the UCPH Jobs Portal and includes:

• Fill out the central application form (even if applying for a single position): https://mlcare.webs.tsc.uc3m.es/open-positions/
• Follow the application procedure for DC11.
• Submit documents in PDF format in English:
• Motivated letter of application/statement of intent (max. one page)
• CV including education, experience, language skills and other relevant qualifications

Deadline for applications: 3 March 2026, 23:59 CET.

For more information on recruitment, eligibility and living in Denmark visit the official pages of the University of Copenhagen and partner institutions.

Contact

Main supervisor: Ole Winther — email: ole.winther@bio.ku.dk

Department/Location

Department of Biology / Centre for Machine Learning in Life Sciences (MLLS), University of Copenhagen