(closed) Master thesis

in Advances in Learning, Inference and Control in Spatial Environments

argmax

The Machine Learning Research Lab (MLRL) is looking for a master student (f/m/d) in the domain of machine learning, localisation, mapping and optimal control in Munich.

The MLRL is part of Volkswagen Group IT and tackles fundamental research in machine learning and optimal control.

We develop new methods for modelling and control of dynamical systems. The focus of this work is learning generative world models, simultaneous localisation and mapping (SLAM) based on onboard sensors of mobile robots as well as the optimal control of those. In a final step, these algorithms are tested on real systems, e.g. robot arms or mobile robots. For this purpose a robot lab with a variety of robotic systems, motion capture systems, a diverse set of sensors and so forth is available. ​

Your Tasks

  • Theoretical and practical development and evaluation of algorithms for the improvement of learning algorithms, state estimation and planning methods.
  • Design, implementation, conduction and documentation of experiments.
  • Possibility of doing a thesis in the context of “Advances in Learning, Inference and Control in Spatial Environments”. ​

Your Qualifications

  • Student (master) in natural sciences or engineering disciplines.
  • Interest in machine learning, robotics and/or computer vision.
  • Very good knowledge of the Python programming language, especially Numpy. Experience with Tensorflow, Pytorch or Jax is a plus.
  • Very good knowledge of tools such as git, LaTex and any Unix shell.
  • Very good knowledge of numerical optimisation, probability theory, information theory, calculus and linear algebra.
  • Proficient in English.
  • Excellent communication skills in written, spoken and visual form. ​

More information on the lab can be found at argmax.ai.

Please apply with your curriculum vitae, certificate of enrolment, and overview of grades at the karriere.volkswagen.de portal.

Announcement at Volkswagen Stellenbörse