If your background is in Machine Learning (ML) and you want your research to directly impact health sciences, this could be your next career move.
Technical University of Denmark (DTU) – specifically DTU Compute – is now inviting applications for a Postdoc in Machine Learning for Biological Data in Kgs. Lyngby, Denmark.
Application deadline: 25 March 2026 (23:59 Danish time)
Start date: 1 May 2026 (or by agreement)
Full-time | 1-year position
Description
This is not just another post-doctoral position, this position collaborate with:
- Bioneer
- DTU Bioengineering
- DTU Health Tech
- University of Copenhagen
DTU aims to build a shared biological data framework and platform to pioneer machine learning models applied to (a) stem cell screening, (b) cell-based therapies, and (c) stem cell differentiation and manufacturing optimization.
You will work on cutting-edge topics such as:
- Modeling Missing Not At Random (MNAR) data
- Bayesian uncertainty quantification
- Deep learning architectures
- Transfer learning between biological applications
This is ML at the frontier of computational biology and regenerative medicine.
Your Role as PostDoc
As part of a diverse, multidisciplinary research team led by Dr. Line H. Clemmensen, you will:
- Develop ML methods for conditional data generation
- Build uncertainty quantification models
- Design transfer learning frameworks
- Publish high-quality scientific papers
- Support data processing and management
- Disseminate models to interdisciplinary collaborators
Who Should Apply?
- A PhD in Machine Learning or a related field
- Strong computational modeling skills
- Creativity and opnness to innovation
- Passion for interdisciplinary research
- Excellent English communication skills
This role is ideal for researchers who want to move beyond algorithm development and contribute directly to human health innovation
Technical University of Denmark (DTU) is globally recognized for excellence in research, innovation, and scientific impact. You will work in an international environment that values: (a) Academic freedom, (b) Collegial respect, (c) Research excellence, and (d) Responsible innovation. Salary and employment terms follow Danis collective agreements