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intelligently to make learning more sustainable and efficient, and a DFG-funded project on distributed optimization and scalable training of deep neural networks, including transformer architectures. We invite
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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for the following two project activities, whereby the distribution of modelling tasks can also be adapted to interests and skills. Mathematical modelling of the functional consequences of microarchitectural changes
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) to the data distributions at hand and evaluation of their predictive performance Comparision to alternative approaches applicable in the small-sample-size regime such as few-shot learning, meta-learning
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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis
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prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable future. These EMS coordinate
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 2 months ago
, and the LISA Distributed Data Processing Centre (DDPC), where our department plays a leading role in waveform generation and the global fit deep analysis. The institute promotes a healthy work-life
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on the sky. This project is dedicated to resolving the longstanding challenge in geodetic VLBI, which is the systematic error caused by source structure (i.e., angular distribution of the brightness
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be