Sort by
Refine Your Search
-
Category
-
Field
-
coding background. You must hold a master’s degree in Computer Science, Electrical and Computer Engineering, Mathematics, or similar. Knowledge in machine learning is desired. We are looking for a driven
-
of very large turbines. Digital controls for noise mitigation. More information on these positions is available below. What we expect: A two-year master’s or equivalent degree in Engineering. A strong
-
computer science, electrical engineering, control systems, or a related field. • Prior experience in reachability analysis, formal verification methods, control theory, or related domains. • Proficiency in
-
engineering. There is no need for previous knowledge in the described fields but a strong motivation to learn and push the boundaries of our current knowledge further. We are looking for an individual with a
-
technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and behavior planning
-
degree in a technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and
-
applicant must have the following: • Masters’ degree in Electrical Engineering, Mechanical Engineering, Physics or a related discipline • Experience with electronic circuits design and testing
-
other chemicals through electrical energy supplied to electrochemical / electro-biotechnological reactor concepts. Such concepts aim at replacing fossil raw materials in value added chains. The advertised
-
08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
-
mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory