Postdoctoral Researcher in Machine Learning

Popis:

At the Department of Systems and Control, 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. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application areas such as medicine, energy systems, biomedical systems, neuroscience, and security. The department maintains a broad network of long-standing international collaborators worldwide, including 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), both stochastic and deterministic formulations. The emphasis 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 designs. We will develop and analyze generally applicable algorithms and models together with our collaborators. The postdoctoral researcher will be jointly supervised by Assistant Professor Dominik Baumann (Aalto University) and, regarding MPC aspects, we will collaborate with Assistant Professor Johannes Köhler at Imperial College London. Technical components 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. Qualification Requirements PhD in machine learning, control engineering, signal processing, or a related field, or a foreign degree deemed equivalent to a PhD in machine learning, control engineering, or signal processing. The degree must be completed by the time the employment decision is made. Ideally, the degree should have been awarded no more than three years ago. When calculating the three-year period, the deadline for applications is the reference point. If there are special circumstances, the degree may have been awarded earlier. Special circumstances include leave due to illness, parental leave, positions of trust within trade union organizations, etc. We seek candidates with: • strong interest in and experience developing new methods in reinforcement learning and/or model predictive control, • ability to communicate technical material effectively in spoken and written 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 essential. Publications at leading machine learning conferences and/or leading control engineering journals are a significant plus. Desirable/Meriting Factors Experience with industry collaboration is valuable. As a person, you are creative, thorough, and have a structured approach. In selecting among applicants, we will assess their ability to independently drive their work forward, to collaborate with others, to maintain a professional approach, and to analyze and work with complex problems. Great weight will be placed on personal qualities and personal suitability. Application Requirements Your application should include: A curriculum vitae (CV), A list of publications, A description of your current and previous research (max 1 page) and proposal 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. Your answers will be used as part of the selection process. About the Position The employment is temporary for 2 years in accordance with the central collective agreement. The position is full-time. Start date: October 1, 2026, or by agreement. Work location: Uppsala. Information about the position is provided by: Professor Thomas Schön, 018 - 471 25 94, [email protected] Welcome to submit your application by August 21, 2026, UFV-PA 2026/1965. Uppsala University is a broad research university with 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 commitment 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/ The employment may be subject to security screening. As a condition for employment in security screening, the applicant must be approved. We decline offers of recruitment and advertising assistance. Applications are received through Uppsala University's recruitment system. Trade union representatives: Saco-S - [email protected], Seko - [email protected], ST (OFR/S) - [email protected]

Přehled

Typ

job

Stav

active

Viditelnost

public

Město

Uppsala

Adresa

box 256

GPS

59.8710738, 17.5946002kopírovatotevřít v ▾

Email

[email protected]

Telefon

018 - 471 25 94

Zobrazení

5

Publikováno

8. 6. 2026

Upraveno

21. 6. 2026

Parametry