Vacant job

At the Department of Systems Engineering, we develop 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 various application areas such as medicine, energy systems, biomedical systems, neuroscience, and security.

The department has a broad network of long-term international collaboration partners worldwide, for example at the University of Cambridge, University of Oxford, Imperial College London, University of Sydney, University of Newcastle, and Aalto University.

Responsibilities

This project focuses on developing, analyzing, and applying new methods in reinforcement learning and model predictive control (MPC), including both stochastic and deterministic formulations. The focus will be on developing methods for robust reinforcement learning and/or MPC formulations. We are particularly interested in environments where the cost or reward function is non-ergodic. In these situations, expected costs/rewards do not capture the behavior of individual rollouts, requiring new constructions. We will develop and analyze generally applicable algorithms and models together with our collaboration partners. The postdoctoral researcher will be jointly supervised by Assistant Professor Dominik Baumann (Aalto University) and, regarding the MPC aspects, we will collaborate with Assistant Professor Johannes Köhler at Imperial College London.

Technical building blocks may include reinforcement learning, state models, MPC, deep learning, and probabilistic modeling in general.

The position may include teaching up to 20% depending on availability and interest. You are expected to be able to teach in Swedish or English.

Qualifications

A PhD in machine learning, control engineering, signal processing, or a closely related relevant field, or a foreign degree assessed as equivalent to a PhD in machine learning, control engineering, or signal processing. The degree must be completed no later than the time the employment decision is made. Preference is given to those who have completed their degree within the last three years. When calculating the three-year timeframe, the starting point is the last day of application. If there are special reasons, such a degree may have been completed earlier. Special reasons include leave due to illness, parental leave, trust positions within trade unions, etc.

We are looking for candidates with:

• a strong interest in and experience in developing new methods in reinforcement learning and/or model predictive control,
• the ability to communicate technical material effectively in oral and written form (English),
• good working knowledge of programming (preferably in Python).
• personal qualities, such as a high level of creativity, accuracy, and/or a structured approach to problem-solving, are crucial.

Publications at leading conferences in machine learning and/or leading journals in control theory are a great plus.

Desirable/Additional Merits

Experience of collaboration with companies is an advantage. As a person, you are creative, accurate, and have a structured approach. In the selection process, we will assess their ability to independently drive their work forward, collaborate with others, maintain a professional attitude, and analyze and work with complex problems. Great emphasis will be placed on personal qualities and personal suitability.

The application should include:

• A curriculum vitae (CV),
• A publication list,
• A description of your current and previous research (max 1 page) and proposals for future research (max 1 page),
• Contact information for two references.

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

About the position

The employment is fixed-term for 2 years according to the central collective agreement. The position is full-time. Start date: October 1, 2026, or as agreed. Location: Uppsala.

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

Please submit your application by August 21, 2026, ref UFV-PA 2026/1965.

Uppsala University is a broad research university with a strong international standing. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all 7,500 employees and 53,000 students who, with curiosity and engagement, make Uppsala University one of the country's most exciting workplaces.

Read more about our benefits and what it is like to work at Uppsala University

https://uu.se/om-uu/jobba-hos-oss/ (https://uu.se/om-uu/jobba-hos-oss/)

The position may be subject to a security clearance. If a security clearance is required, it is a condition of employment that the applicant is approved.

We decline offers of recruitment and advertising assistance.

Applications are received in Uppsala University's recruitment system.

Trade union representatives: Saco-S - [email protected], Seko - [email protected], ST (OFR/S) - [email protected]