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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
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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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specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: www.fz-juelich.de/en/judocs Targeted services for international employees, e.g. through our
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the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for submanifolds and their temporal dynamics during behavior Leverage dimensionality reduction and
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experience in machine learning and parallel computing Good organisational skills and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow
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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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for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs Targeted services for international employees, e.g. through our International