57 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships at Technical University of Munich in Germany
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and
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Manipulation in Cluttered and Dynamic Environments (ID: TUEILSY-PHD20240930-SCMM) A more detailed topic description can be found at https://www.ce.cit.tum.de/lsy/open-positions/open-phd-positions/ . Requirements
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to challenging questions in the field of computational material design, especially with the help of CALPHAD-based methods. For further development of our simulation environment (https://github.com/cmatdesign
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our privacy policy on collecting and processing personal data in the course of the application process pursuant to Art. 13 of the General Data Protection Regulation of the European Union (GDPR) at https
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over the world, especially the USA, the UK, and Germany. Your Profile: Excellent university degree in engineering, chemistry, materials science, physics, electrochemistry or a similar discipline Strong
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Engineering, or a related discipline - Solid understanding of fluid dynamics and/or electromagnetism - Programming experience (preferably Python) - Interest in machine learning and scientific computing
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23.10.2023, Academic staff The Technology and Innovation Management (TIM) Group at the TUM School of Management of the Tech-nical University of Munich, headed by Prof. Dr. Joachim Henkel, is
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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