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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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platform for cancer, in collaboration with experimental partners. Your tasks: Development and application of interpretable large-scale hybrid mechanistic- and machine learning-based mathematical models with
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parameter space of the electrolysis processes. DoE is required for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map
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. Ideally you have Programming skills and knowledge on machine learning and statistical data evaluation, creation of scientific programme codes using common software packages (MATLAB, Python, R) Simulation
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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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Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ | Potsdam, Brandenburg | Germany | 2 months ago
data sets, and experience with machine learning is a plus Ability to work within a team, excellent interpersonal and communication skills Attention to detail and organisational skills Excellent English
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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