PhD Fellowship in Simulation-Supervised Machine Learning for Biology

Start date is (expected to be) 1 January 2027 or as soon as possible thereafter. The position is for 3 years.

The project

This PhD project will develop simulation-supervised machine learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to train models on synthetic data generated by biophysical simulations by developing simulations that can be automatically tuned to be realistic enough that the trained models generalize to real experimental data.

The project will combine differentiable programming, biophysical simulation, differentiable rendering or signal generation, and machine-learning architectures for reducing the simulation-to-reality gap. A central challenge is to automatically tune simulations so that synthetic datasets match the variability, noise, and structure of real, unlabelled biological datasets. This will enable machine-learning models to learn from perfect synthetic ground truth rather than from limited human annotations.

Possible methodological directions include differentiable simulations in JAX or PyTorch, neural or physics-based rendering, generative distribution matching, and domain adaptation. The exact direction of the PhD project will be defined in dialogue with the student and will depend partly on their interests and background. One application area is chromatin organization, where simulations of DNA or chromatin structure could support machine-learning analysis of microscopy data, including segmentation, loop quantification, nucleosome spacing, or structural inference.

Who are we looking for?

We are looking for highly motivated candidates with a strong background in computer science, machine learning, scientific computing, physics, applied mathematics, or a closely related field.

Relevant experience may include one or more of the following:

• machine learning, deep learning, or generative modelling
• computer vision or biological image analysis
• differentiable programming, for example in JAX or PyTorch
• numerical simulation, physics-based modelling, or scientific computing
• domain adaptation, synthetic data generation, or simulation-to-reality methods
• microscopy data, biophysical systems, or computational biology.

Our section

The project will be hosted by the IMAGE section, which performs research in machine learning, computer vision, and simulation. The section is a part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen. We are located in Copenhagen.

PhD supervisors are Julius B. Kirkegaard. Email: juki@di.ku.dk and Jon Sporring Email: sporring@di.ku.dk

Qualifications needed for the regular programme

To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. [relevant educations]. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.

Terms of employment in the regular programme

Employment as PhD fellow is full time and for maximum 3 years.

Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of Science, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

Terms of appointment and payment accord to the agreement between the Danish Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. The position is covered by the Protocol on Job Structure.

Application and Assessment Procedure

Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.

Please include:

1. Motivated letter of application (max. one page)
2. Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
3. Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position. If possible, also include a note on your grades from the diplomas.
4. Include a publication, project, or code that you are proud of.
5. Copy of your Bachelor or Master thesis (if available).
6. Name, email of reference(s).
7. Publication list (if available).
8. Reference letters (if available).

Application deadline

The deadline for applications is 15 September 2026, 23:59 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 abovementioned requirements.

The further process

After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above-mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/.

Questions

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

General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/

The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.