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. The Leibniz-LSB@TUM comprises a unique and world-leading research profile at the interface of Food Chemistry and Biology, Chemosensors and Technology, and Bioinformatics and Machine Learning. Leibniz-LSB@TUM’s
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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-learning prediction models” with the following focus areas: Design and development of methods for drifter detection in self-learning AI models Evaluation using real data sets from photovoltaic systems and
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, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and automatic code optimization
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the REACT Doctoral Network. In this context, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and
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planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world
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), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open