Doctoral Students in Machine Learning for Decision Making KTH Royal Institute of Technology in Sweden

KTH Royal Institute of Technology is seeking up to two doctoral students to join the Division of Decision and Control Systems for research in machine learning for decision making. The project focuses on developing advanced machine learning models to support decision-making in complex systems, with applications in networked control systems, cyber-physical systems, system identification, and transport systems. The division is internationally recognized and collaborates with leading industrial partners such as Hitachi Energy, ABB, Ericsson, and Scania, as well as top academic institutions including Caltech, MIT, UC Berkeley, and Stanford.

The research is conducted within the Wallenberg AI, Autonomous Systems and Software Program (WASP), providing a dynamic and interdisciplinary environment. Supervision will be provided by Professor Karl Henrik Johansson and Matthieu Barreau. The position offers a full-time monthly salary according to KTH’s doctoral student salary agreement, along with employee benefits and support for relocation and settling in Stockholm.

Applicants must have a relevant master's degree or equivalent, strong background in machine learning, mathematics, and control theory, and proficiency in English. The selection process emphasizes academic excellence, relevant coursework, and personal skills such as independence, collaboration, and analytical ability. The position is for up to four years, with annual or biannual renewal, and may include up to 20% teaching or administrative duties.

Applications must be submitted through KTH's recruitment system and include a CV, application letter, diplomas and grades, proof of language proficiency, representative publications or technical reports, and contact information for at least three references. The deadline for applications is November 21, 2025. This is an excellent opportunity to join a leading technical university and contribute to cutting-edge research in machine learning and decision making.