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) Expertise in further programming languages (in particular C++), GPU programming, parallel programming or high-performance computing are highly valued Keen interest in neuroscience is essential Experience with
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to enable quantitative, high-resolution, time-lapse monitoring of soil properties and will be implemented using high-performance parallel computing. This PhD position offers the opportunity to work at the
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++) Good 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
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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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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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only
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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