57 programming-"https:" "https:" "https:" "https:" "https:" positions at Technical University of Munich
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, or nanostructured interfaces is advantageous • Familiarity with quantitative data analysis, image analysis, or scientific programming is beneficial • Strong problem-solving abilities, rigorous experimental practice
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• Interdisciplinary applications ranging from biomechanics and geophysics to fluid and polymer models Further information can be found here: https://www.math.cit.tum.de/math/forschung/gruppen/numerical-analysis
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and autonomous experimentation, ideally including experience with robotic liquid handling, hardware integration, or experimental workflow development • Experience with scientific programming and data
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, or a related field. We expect knowledge of mathematical modeling, numerical simulation, or control engineering, as well as programming experience, for example in Matlab or Python. Initial experience with
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pursues for the second time the visionary doctoral program with high socio-economic relevance on the topic of “The Proteomes that Feed the World” with the aims: 1. To train and develop future leaders in
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have skills in CAD modelling, geometry processing, and programming, as well as prior knowledge in digital fabrication. Finally, you are fluent in English (German skills are welcome but not mandatory
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excitations and excitonic effects using advanced Wannier-based methods * Quantum transport in polymer materials with electron–phonon coupling Full details and application instructions: https://www.ch.nat.tum.de
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learning is required; familiarity with contrastive learning, computer vision, or geospatial data is welcome Very good programming skills (Python, C++, etc.) are essential Fluent English language skills
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) • Solid wet-lab experience and experience in library preparation for next-generation sequencing • Experience working in a Linux/Unix environment and R programming • Written and oral communication skills in
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programming skills in Python; initial experience with machine learning frameworks such as PyTorch or TensorFlow Initial practical experience from a master's thesis, study projects, internships, or open-source