Postdoctoral position in Machine Learning with a focus on models evolving over space and time

Are you interested in working with machine learning methods with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favorable working conditions? We welcome you to apply for a postdoc position at Uppsala University. 

Uppsala University has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

At the Division of Systems and Control, we develop and analyze both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical systems, neuroscience, and safety and security.

We have a wide network of long-standing international collaborators all around the world, for example at the University of Cambridge, University of Oxford, University of Sydney, University of Newcastle and Aalto University.

We offer a postdoc position to explore and develop Machine Learning models evolving over space and time and to make use of these models to understand the organization of DNA and its relation to the dynamic 3D-structured chromosomes. The successful candidate will form a part of our new NEST initiative funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the Wallenberg National Program for Data-Driven Life Science (DDLS). Our project, Learning 3D Genome Dynamics from Heterogeneous Data, is a 5-year collaboration between researchers at Uppsala University and Karolinska Institute. The overall objective is to develop and make use of machine learning methods to help us understand the organization of life.

Project description and Duties
This project focuses on developing, analyzing and using probabilistic methods for dynamic phenomena evolving over space and time based on measurements from different and complementary sources. We will develop generally applicable machine learning models and methods driven by the data-rich experiments from our collaborators. The real-world use-case is to learn the rules that govern the dynamics of the bacterial chromosome structure.

Technical building blocks could include state-space models, generative models in the form of diffusion models, deep learning, optimal transport, and probabilistic modelling in general. Computer vision can also be included if there is interest.

The postdoc position involves the development of theory and probabilistic methods for phenomena evolving over time and space. Related to this is the task of deriving algorithms that can be used to learn the unknown model parameters from the measured data. The position involves collaboration within the NEST project partner groups of Johan Elf and Magda Bienko.

The position can include teaching up to 20% depending on availability and interest.

Requirements
PhD degree in machine learning, signal processing, computer vision, computational statistics or another nearby and relevant field or a foreign degree equivalent to a PhD degree in machine learning, signal processing, computer vision, computational statistics or another nearby and relevant field. The degree needs to be obtained by the time of the decision of employment. Priority will be given to applicants who have completed their degree no more than three years before the deadline for applications. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

We are looking for candidates with

• an interest in and documented experience of developing Machine Learning methods and models,
• ability to use deep learning,
• ability to communicate technical material effectively in oral and written form (English),
• good working knowledge in programming (preferably in Python),
• personal characteristics, such as a creativity, thoroughness, and/or a structured approach to problem-solving are essential.

Experience in modeling dynamic phenomena over time and space is an advantage. Publications at leading conferences in machine learning is a strong plus. You are expected to be able to teach in Swedish or English.

Additional qualifications
Experience of interdisciplinary research is good. Experience and knowledge in one or more of these subjects are valued: machine learning, dynamical systems, system identification. When selecting among the applicants we will assess their ability to independently drive their work forward, to collaborate with others, to have a professional approach and to analyze and work with complex problems. Great emphasis will be placed on personal characteristics and personal suitability.

Application: The application must contain:

1. A curriculum vitae (CV),
2. A copy of relevant grade documents (translated into Swedish or English),
3. A list of publications
4. Up to five selected publications in electronic format
5. A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page).
6. Contact information for two references.
7. A cover letter briefly describing your motivation for applying for this position and the earliest possible employment date (max 1 page).

About the employment
The employment is a temporary position of 2 years according to central collective agreement. Full time position. Starting date March 1, 2026 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Professor Thomas Schön, 018 - 471 25 94, thomas.schon@uu.se 

In this recruitment we have replaced the cover letter with questions that you are asked to answer when making your application. The answers will be used as a part of the selection process.

Please submit your application by 23 January 2026, UFV-PA 2025/3868.

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