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Information Engineering Machine Learning and Data Science Embedded Systems and hardware-oriented development Modeling and simulation of complex systems Experience with Python, MATLAB, and/or C/C++ Strong
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implementation and commissioning Knowledge of machine learning or reinforcement learning applied to control systems is a strong asset Experience with accelerator technology, specifically photocathodes is an asset
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assets include strong programming skills, experience with modern machine-learning techniques such as neural simulation-based inference or transformer architectures, and familiarity with fitting methods and
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 2 months ago
coordination between real-time hardware and software and novel, machine learning-based predictive algorithms. The hardware for the METIS AO system, along with an ELT telescope simulator, has been set up in
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to design and control biological systems in a targeted manner. An integral part of BioSysteM is the AI Core Unit, a cross-institutional support unit. This unit aims to make machine learning (ML) and
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optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware
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influencing factors and improve the accuracy, robustness and energy efficiency of intelligent sensing systems. Apply AI as an engineering tool: Use signal processing, statistical methods and machine learning
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involving Prof. Dr. Michael Bader (TUM CIT, Hardware-aware algorithms for HPC) , Prof. Dr. Felix Dietrich (TUM CIT, Physics-enhanced Machine Learning) , and Prof. Dr. Hartwig Anzt (TUM CIT, Computational
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 29 days ago
interacts with the prefrontal cortex to integrate interoceptive signals and guide higher-order cognition. Combining ultra-high-field layer fMRI at 9.4 Tesla, diffusion MRI, and machine learning in humans with