66 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "https:" positions at Technical University of Munich
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or School of Management and participation in the related Graduate School training programs. Qualifications The applicants should possess: an excellent or very-good university degree in economics strong
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kinetic mechanisms and key elementary reactions involved. Addressing this shortcoming is the goal of this project. Please visit the DFG research unit description for more information on the topic https
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power systems in the CoSES lab at the Technical University of Munich. Previous Work https://mediatum.ub.tum.de/doc/1731060/g5zgxaj96lcyhh8gh6le1xbuu.Wetzlinger-2023-TAC.pdf https://mediatum.ub.tum.de/doc
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programming and tools such as MATLAB/Simulink. Fluency in spoken and written English; German language skills are a plus. Experience in team leadership, excellent communication and organizational skills
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in production engineering issues and their investigation Enjoyment of supervising and programming technical systems Purposefulness and independent working style Creativity and willingness to experiment
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spectrometry. ProteomicsDB (https://www.proteomicsdb.org/) is an internationally well-reputed publicly available database that provides information about proteins and other bio-molecules initially centered
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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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software programming in C++/Python, ROS and MATLAB. Excellent command of English; German language skills are a plus. High motivation and the ability to work independently as well as within an international
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will soon obtain the degree are also welcome to apply) - strong background in machine learning - proficiency in Python programming - experience with molecular simulations and knowledge of statistical
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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