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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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. Your Profile: A Masters degreee with a strong academic background in physics, mathematics, computer science, or a related field Proficiency in at least one programming language (Python, C
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, dental, life and disability insurance, tuition waivers for employees and their families, 12 official holidays, immediate leave accrual, and a choice of retirement programs with university contributions