98 programming-"St"-"FEMTO-ST"-"UCL"-"St" positions at Technical University of Munich in Germany
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tools (e.g., Python programming) is an advantage. You enjoy working in an international team and have good communication skills. Proficiency in spoken and written English is required. For more information
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participation in the related Graduate School training programs. Qualifications The applicants should possess: an excellent or very-good university degree in economics, business studies, agricultural sciences with
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technologies to fundamental physics questions. The advertised positions will be part of the project “QS-Gauge: quantum simulation of lattice gauge theories”, funded by the Emmy Noether programme of the DFG
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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
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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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public service. • Knowledge in and practical experiences with programming (e.g., Python, Java, Julia, etc.) • A strong interest in scientific research with the goal of obtaining a doctoral degree • High
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prestigious Humboldt Research Fellowships for the Logistics and Supply Chain Management research group at TUM School of Management. With the scouting program, the foundation aims to attract highly talented
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Germany for 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
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, Data Science, or a closely related discipline Strong interest in interdisciplinary research at the interface of Artificial Intelligence, Data Science, and Life Sciences Excellent programming skills
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programming language, e.g. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred