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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for
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data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ 30 days of annual leave (depending on agreed working time arrangements) and provision for days off between
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pipetting, buffer and solution preparation, PCR, gel electrophoresis, and sterile technique Proven ability to manage multiple tasks and keep parallel experiments on track Strong teamwork and communication