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Technical University of Munich

PhD – Safe Active-Learning Controllers Using Gaussian Processes

We are looking for scientific staff in the field of active learning with Gaussian processes. The doctoral position is embedded in the DFG project ALeSCo, aiming at the transfer of powerful static machine learning methods to control tasks in dynamic environments.

You will develop novel methods that enable safety-critical systems to learn their behavior during operation while still provably meeting safety requirements. A Bayesian modelling approach allows for systematically incorporating prior knowledge on the behavior of the system.

Excellent applicants have a strong disciplinary background, are enthusiastic, creative, and eager to work independently within an interdisciplinary environment. The position is remunerated at pay grade E13 TV-L and includes the option to pursue a doctorate.

About us:

At the Chair of Information-Oriented Control we focus on research and teaching of control and optimization of cooperative, networked, and distributed dynamical systems. We develop novel methods and tools for the analysis and control of such systems, taking particularly into account model uncertainties as well as limitations pertaining to acquisition of data, communication, and computation. We apply our methods mainly to human-robot-teams, haptic assistance, cyber-physical systems, and infrastructure systems. While our core competence is control engineering and robotics, we have some interdisciplinary collaborations with the fields of psychology (in human-robot interaction) and communications (in networked control systems). Many of the developed methods are experimentally validated in our multi-robot lab.

To learn more about our research and ongoing projects, we invite you to read our current publications and visit the websites of our individual team members. If you have questions about our group culture or specific research topics, please feel free to contact us. However, make sure to first check the FAQs to see if your question has already been answered.

Your profil:

  • Completed university degree in engineering, computer science, mathematics, physics, or a comparable field.
  • Enthusiasm, creativity, and the ability to work independently and responsibly.
  • Convincing academic record.
  • Solid knowledge of control theory and machine learning, ideally also statistics and robotics.
  • Experience with higher-level programming languages such as C++, Python, MATLAB, or Julia.

We offer:

Application:

Please send your application including a motivation letter, your complete CV, grades, relevant certificates, and some of your publications to

Univ.-Prof. Dr.-Ing. Sandra Hirche
Lehrstuhl für Informationstechnische Regelung
Technische Universität München
80290 München

Electronic applications in a single pdf file by Email to applications.itr(at)xcit.tum.de

TUM is especially encouraging minorities and women to apply, because of its strong commitment to diversity in engineering education, research, and practice.

 

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