20 programming-"https:"-"Inserm"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "https:" PhD scholarships at Technical University of Munich
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are expected to have a M.Sc. or equivalent in engineering, applied math, physics or similar and a solid background in mechanics and numerical methods. Programming skills (any language) are a plus. If
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skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX or PyTorch, is a plus. In addition to above-average interest in the topic
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a related field ▪ Strong knowledge in wireless communication systems, signal processing, or radar systems ▪ Proficiency in at least one programming language (e.g. Python) ▪ Interest in hands
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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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skills in programming and strong experience in computational geometric modelling. Expertise in the field of Additive Manufacturing, Construction Robotics, and/or Artificial Intelligence is very beneficial
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Profile The ideal applicant has a strong background in bioinformatics or computational chemistry, as well as data analysis and solid English-language skills. Experience with programming is highly
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an interdisciplinary career skills programme across Europe. The Technical University of Munich (TUM) is one of the best universities in Europe. It is characterised by excellence in research and teaching
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academic performance. Experience with stochastic methods, risk and reliability analysis, and data analysis. Programming experience in Python, MATLAB, C/C++, or a similar language. Strong analytical
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to design and evaluate programs that enhance community wellbeing. The project is a collaboration between the University of Global Health Equity (Rwanda), TUM, NYU Abu Dhabi, and the Government
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning