47 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions at Forschungszentrum Jülich
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applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under various operating conditions and develop strategies
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the INW-1 machine learning team on data handling, online analysis, design of experiments (DoE), and data categorization to enable efficient and automated evaluation of operando experiments Collaboration
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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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, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with experiments, including
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, numerical methods, or machine learning approaches is an advantage. Fluent command of written and spoken English is necessary; German is an advantage but not required. High degree of independence, motivation
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vision models Experience with event-based cameras, neuromorphic vision concepts, spiking neural networks, and/or neuromorphic computing is a plus Experience with, or willingness to learn, ROS 2 for robotic
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on these considerations, solutions will be developed and implemented to create an advanced testing platform for the comprehensive evaluation of the long-term stability of invasive brain-computer interfaces. Your task will