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KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.
Third-cycle subject: Electrical Engineering
We are looking for doctoral students with a strong background in mathematics (statistics, probability theory and optimization) and an interest in machine learning and control. The successful candidates will work on a recently granted project on Statisical and Adversarial Learning in Continuous System Control. The project will be supported by WASP (Wallenberg Autonomous System Program). The research will be conducted in a collaborative manner in Prof. Anders Rantzer’s group at Lund university and Prof. Alexandre Proutiere’s group at KTH.
The project is concerned with sequential decision making in systems whose dynamics are initially unknown, i.e., with adaptive control or Reinforcement Learning (RL) when using the control engineering and machine learning terminologies, respectively. Most existing RL algorithms are designed to learn and control stochastic dynamical systems with finite state and action spaces. The statistical learning theory for such systems is well developed and understood, as we have, since the 90’s, fundamental performance limits satisfied by any algorithm, as well as joint learning and control strategies achieving these limits, at least asymptotically when the time-horizon grows large. This theory becomes irrelevant for systems with large state or action spaces: without imposing and leveraging any specific structure on the system dynamics, learning seems impossible in a reasonable time. Continuous systems have infinite state and action spaces, but follow physics-based models,and hence exhibit a-priori known structural properties that can be exploited to speed up the learning process. The project aims at developing a statistical learning theory of adaptive control in continuous systems.
What we offer
To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:
In order to succeed as an doctoral student at KTH you need to be goal oriented and persevering in your work. In the selection of the applicants, the following will be assessed:
The successful applicant is expected to hold, or to be about to receive, an MSc degree in enengineering physics, electrical engineering, computer science, or mathematics. A specialization in statistics/probability, algorithm design, control, or machine learning is an asset.
Target degree: Doctoral degree
Information regarding admission and employment
Only those who are or have been admitted to third-cycle studies may be employed as a doctoral student. The term of the initial contract may not exceed one year and may thereafter be extended. Doctoral students may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position.
You will find contact information for union representatives on KTH's website.
Doctoral section (Students’ union on KTH Royal Institute of Technology)
You will find contact information for doctoral section on the section's website.
Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/entral European Summer Time).
Applications must include the following elements:
Gender equality, diversity and zero tolerance against discrimination and harassment are important aspects of KTH's work with quality as well as core values in our organization.
For information about Processing of personal data in the recruitment process please read here.
We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.
Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.Type of employment: Temporary position longer than 6 months
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