PHD - machine learning for robot manipulation

neues Angebot (05/03/2020)

stellenbeschreibung

Job Description Company Description Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. The Robert Bosch GmbH is looking forward to your application! Job Description At the BCAI at Bosch, we are building a new group for machine learning for robot manipulation. The group will be located at the University of Tubingen and led by Prof. Gerhard Neumann. The BCAI is a leading lab in machine learning and artificial intelligence which regularly publishes at top venues such as NIPS and ICML. Our aspiration for the new group is to become one of the leading robotics groups in the world. We aim to make robot manipulation useable in the real world, such as cluttered unknown environments consisting of unknown, possibly deformable objects where fine and forceful manipulations need to be applied to the scene such as object assembly. Our research will be focused on the development of new learning algorithms that are useable in these scenarios. We are looking for new PhD students in the following areas:

• Deep learning methods for robot vision and robot control * Model-based and model-free deep reinforcement learning * Imitation learning and inverse reinforcement learning * Hierarchical reinforcement learning * Uncertainty quantification in deep learning models * Learning from human feedback * Information-theoretic learning methods * Efficient robot movement representations * Skill libraries and hierarchical skill learning * Teleoperation and shared control for user guidance Qualifications * Education:

Excellent Master degree in computer science, electrical engineering, mechanical engineering, math or related fields

* Personality

:

highly motivated to work on these challenging topics and develop a personal research agenda * working practice:

team-oriented, open-minded and inquisitive * experience and knowledge:
excellent skills in python, ros, c++ or matlab and excellent math skills, knowledge in the areas of machine learning and robotics is preferential * languages:

fluent in english (written and spoken) additional information please submit all relevant documents (incl. curriculum vitae, certificates). the final phd topic is subject to your university. duration:

3 years need support during your application? kevin heiner (human resources) +49 711 811 12223 need further information about the job? kevin heiner (functional department) +49 711 811 12223 qualifications:

education:

excellent master degree in computer science, electrical engineering, mechanical engineering, math or related fields. personality:

highly motivated to work on these challenging topics and develop a personal research agenda working practice:

team-oriented, open-minded and inquisitive experience and knowledge:
excellent skills in python, ros, c++ or matlab and excellent math skills, knowledge in the areas of machine learning and robotics is preferential languages:

fluent in english (written and spoken) responsibilities:
at the bcai at bosch, we are building a new group for machine learning for robot manipulation. the group will be located at the university of tubingen and led by prof. gerhard neumann. the bcai is a leading lab in machine learning and artificial intelligence which regularly publishes at top venues such as nips and icml. our aspiration for the new group is to become one of the leading robotics groups in the world. we aim to make robot manipulation useable in the real world, such as cluttered unknown environments consisting of unknown, possibly deformable objects where fine and forceful manipulations need to be applied to the scene such as object assembly. our research will be focused on the development of new learning algorithms that are useable in these scenarios. we are looking for new phd students in the following areas:

deep learning methods for robot vision and robot control model-based and model-free deep reinforcement learning imitation learning and inverse reinforcement learning hierarchical reinforcement learning uncertainty quantification in deep learning models learning from human feedback information-theoretic learning methods efficient robot movement representations skill libraries and hierarchical skill learning teleoperation and shared control for user guidance

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