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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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are predominately held in English. Full-time / part-time part-time (study alongside work) full-time Programme duration 8 semesters Beginning Only for doctoral programmes: any time Additional information on beginning
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diagnosis, and knowledge of the operation of helicopter systems. • Confident handling of Python and common data science tools. • Knowledge of high-performance computing and machine learning. • Fluency in
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Understanding of physical principles of MRI Ideally experience with machine learning and numerical methods Expertise in programming (C/C++, Python, Matlab) Excellent problem-solving skills and a collaborative
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06.10.2023, Wissenschaftliches Personal The PhD position is on safety verification of Cyber-Physical Systems at the intersection between control theory and machine learning. The position is full
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processes, and the application of AI methods in engineering. Description: Nowadays, computer-aided manufacturing (CAM) methods are used to a large extent for the production of complex machine components, in
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg