Postdoc positions in Scientific Machine Learning for Model
Conditions
Applications must be received no later than 31 January 2026.
Shortlisting is used; the head of department selects candidates for evaluation after the deadline.
Referees must be agreed upon beforehand and have sufficient time to provide letters before the deadline.
Letters of reference received after the application deadline will not be considered.
Salary depends on seniority as agreed between the Danish Ministry of Taxation and the Confederation of Professional Associations.
Deadline
Formalities and salary range
The university offers an inspiring research and teaching environment to its 38,000 students (FTEs) and 8,300 employees, and has an annual revenues of EUR 935 million.
Tasks
Develop and implement novel algorithms at the intersection of computational physics and machine learning for data-driven discovery of physical models.
Work primarily with scientific machine learning methods, including symbolic regression and neural networks.
Apply algorithms to discover new models in robotic control, mechanics of materials, fluid mechanics, and biological systems in collaboration with researchers and companies.
Implement methods in high-performance open-source libraries to ensure scalability and availability for researchers and engineers.
Requirements
PhD in Computer Science, Computer Engineering, Artificial Intelligence, Physics, Mathematical Engineering, Mechanical Engineering, or a similar field.
Submit an application in English including a CV, degree certificate, publication list, research plan, research activities, and teaching portfolio with verified experience.
Meet the qualification requirements and job content specified in the Memorandum on Job Structure for Academic Staff at Danish Universities.
The group currently consists of 3 PhD students.
Preferred
Deep knowledge of neural network architectures (plus: PINNs, neural operators, transformers/LLM) and NN training.
Strong background in machine learning and data science.
Strong Python programming skills (plus: C++ or Julia) and knowledge of scientific computing libraries (numpy, scipy, JAX) and machine learning libraries (scikit-learn, PyTorch).
Flexibility to adapt to different application fields.
Independence and time management skills.
Professionalism, strong research ethics, and commitment to research quality.
Benefits
Interdisciplinary environment with national, international, and industrial collaborators.
Opportunity to co-supervise PhD and MSc students.
Workplace characterized by professionalism, equality, and a healthy work-life balance.
Relocation services and career counselling for expat partners.
Access to the Junior Researcher Development Programme for postdoc career development.
Other information
These are 1–year positions from 1 May 2026 or as soon possible.
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