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Field
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for prototyping. Interest and affinity for high-performance computing are necessary for the position. You should have experience with the roofline model and familiarity with a profiler . Experience with GPUs is a
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of e.g. proteomics, lipidomics, or metabolomics data Requirements: Candidates must hold a master’s degree in Data Engineering, Data Science, Bioinformatics, Informatics, or a related discipline. Essential
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renewable generation like solar and wind, flexible loads like heat pumps and electric vehicles, and distributed energy storage like stationary batteries and hydrogen storage to maximize energy efficiency
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(mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and behavior planning, control) -Very good
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(mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and behavior planning, control) -Very good
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available in the further tabs (e.g. “Application requirements”). Programme Description The merit-based programme gives outstanding international students the opportunity to study at Universität Hamburg
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. The project is jointly supervised by Prof. Dr. Ralf Jänicke (Institute of Applied Mechanics, Technische Universität Braunschweig) and PD Dr. Stefan Kollmannsberger (Chair of Computational Modeling and
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more than 12 months in the 36 months immediately before the recruitment date. Nationality: Open to all nationalities. Doctoral Programme Enrolment: You must be willing to enrol in a doctoral programme at the
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studying mechanical engineering, computer science, industrial engineering or a comparable subject Experience in the field of machine learning is an advantage A high degree of initiative and team spirit Very
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Learning algorithms for EDA in terms of Computational Complexity, Performance Scores, etc. To learn more about our previous work, please check out our website ( www.cda.cit.tum.de/research/machine_learning