Postdoc in Machine Learning, Autonomous Decision-making, and Optimization

Postdoc in Machine Learning, Autonomous Decision-making, and Optimization

KTH - Royal Institute of Technology

Stockholm, Sweden

Job description

We are looking for 2 driven postdocs to join our research group at the Division of Decision and Control Systems at KTH. Our group conducts fundamental research at the intersection of machine learning, optimization, and decision-making, with applications in next-generation networked systems.

As a postdoc, you will have the unique opportunity to define your research agenda within our broad scope. The position offers flexibility to focus on one of two main tracks, depending on your background and interests:

• Fundamental Algorithms & Theory: Developing rigorous mathematical frameworks for distributed optimization, learning, and decision-making under uncertainty. This track offers a high degree of freedom to explore new theoretical directions;
• Distributed Intelligence for 6G: Designing the algorithmic foundations for 6G wireless networks. You will work on distributed learning (e.g., federated learning) over wireless edges, focusing on the interplay between communication, computation, and control.

You will join a dynamic team with strong funding (e.g., WASP, KAW, VR) and an extensive international network. We aim for publications in top-tier venues such as NeurIPS, ICML, AISTATS, IEEE Transactions, and top control/signal processing conferences. Ideally, this position will serve as a stepping stone for a future tenure-track career.

You will be supervised by Professor Mikael Johansson and collaborate closely with PhD students and senior researchers. The position can include a limited amount of teaching or supervision (no more than 20%), giving you ample time to focus on your research portfolio.

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree in electrical engineering, computer science or applied mathematics. This eligibility requirement must be met no later than the time the employment decision is made;
• A strong mathematical background in optimization, linear algebra, and probability;
• Documented research expertise in machine learning, control theory, or applied mathematics, demonstrated by publications in high-quality international venues;
• Excellent written and oral communication skills in English, for day-to-day work;
• Personal skills: We are looking for a candidate with a strong drive and scientific curiosity. You should be capable of working independently to drive your own research processes while also possessing the collaborative abilities required to work in a team and co-supervise younger researchers.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline;
• Experience with distributed optimization, decentralized and federated learning;
• Prior knowledge of wireless communications is not a strict requirement for candidates focusing on the theoretical track, provided they have a strong methodological background;
• Proven ability to publish in top-tier conferences (e.g., NeurIPS, ICML, AAAI) or leading IEEE journals. Programming skills (e.g., Python, C/C++, Matlab) relevant for validating research results;
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality.

Great emphasis will be placed on personal skills.