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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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Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods are...
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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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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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for information-driven experiment steering (closed-loop control) Work in an interdisciplinary team of engineers, computer scientists, and life scientists Present your work at international conferences and learn
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interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state-of-the-art machine learning and
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into the open-source CADET simulation framework, enabling fully predictive process simulations without extensive experimental calibration. Embedded in the Helmholtz Graduate School for Data Science in Life, Earth
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machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux, Windows) skills