Postdoctoral Researcher in Machine Learning and DNA Structure

The Department of Information Technology holds a leading position in both research and education at all levels.

For more information about us, visit the Department of Information Technology's website (https://www.it.uu.se).

At the Department of Systems Engineering, we develop both theory and concrete tools for designing systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms.

We offer a postdoctoral position to explore and develop machine learning models that evolve over time and space, and to use these models to understand the organization of DNA and its relation to the dynamic 3D-structured chromosomes. The position is 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.

Job Responsibilities

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

Technical building blocks may include state models, generative models in the form of diffusion models, deep learning, optimal transport, and probabilistic modeling in general. Computer vision may also be included if there is interest. The position may involve teaching up to 20% depending on availability and interest.

The 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 unknown model parameters from measured data. The position entails collaboration within the NEST project's partner groups led by Johan Elf and Magda Bienko.

Qualifications

A PhD in machine learning, signal processing, computer vision, computational statistics, or a closely related field, or a foreign degree deemed equivalent to a PhD in machine learning, signal processing, computer vision, computational statistics, or a closely related field. The degree must be completed by the time the employment decision is made. Primarily, candidates who have obtained their degree within the last three years will be considered. When calculating the three-year time frame, the deadline for applications is the starting point. If there are special reasons, such a degree may have been obtained earlier. Special reasons refer to leave due to illness, parental leave, union duties, etc.

We are looking for candidates with:

• a strong interest in and experience with developing new machine learning methods and models,
• the ability to use deep learning,
• the ability to communicate technical material effectively in both oral and written form (English),
• good programming skills (preferably in Python).

Personal qualities, such as a high level of creativity, accuracy, and/or a structured approach to problem-solving, are essential.

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

Desirable/meritorious

Experience in interdisciplinary research is beneficial. Experience and knowledge in one or more of the following subjects are valuable: machine learning, dynamic systems, system identification. In the selection among applicants, we will assess their ability to independently drive their work forward, collaborate with others, maintain a professional approach, and analyze and work with complex problems. Great emphasis will be placed on personal qualities and suitability.

Application should include:

• A curriculum vitae (CV),
• Publication list,
• Up to five selected publications in electronic form,
• Description of your current and previous research (max 1 page) and proposal for future research (max 1 page),
• Contact information for two references,
• A personal letter briefly explaining why you are applying for this position and indicating the earliest possible start date (max 1 page).

About the Employment

The employment is temporary for 2 years according to the central collective agreement. The scope is full-time. Start date is March 1 or by agreement. Location: Uppsala.

For inquiries about the position, please contact: Professor Thomas Schön, 018 - 471 25 94, [email protected]

In this recruitment, we have replaced the personal letter with questions that you will answer in connection with your application. The answers will be used as part of the selection process.

Welcome with your application by January 23, 2026, UFV-PA 2025/3868.

Please note that this is a shortened version of the advertisement. To see the full advertisement, please click on “Apply here” or visit Uppsala University's website for job advertisements.

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Read more about our benefits and what it's like to work at Uppsala University here (https://uu.se/om-uu/jobba-hos-oss/).

The employment may be subject to security clearance. A prerequisite for employment during security clearance is that the applicant is approved.

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