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• Information-theoretical analysis of entanglement-supported classical communication, • Encoding protocols for entanglement-assisted classical communication, • Protocols for interleaving transmission in networks
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policy. The Institute works on an interdisciplinary basis and is highly networked both nationally and internationally. About the position We are looking for a 100% postdoctoral research associate beginning
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will train a physics-informed neural network (PINN) for fast, precise predictions of pressure, density, and velocity fields. The project also includes producing feed spacer prototypes through 3D printing
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is conducted in collaboration with partners in Europe, the Argentinean National Research Council (CONICET) and the International Livestock Research Institute (ILRI), based in Kenya and their network
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partners in Europe, the Argentinean National Research Council (CONICET) and the International Livestock Research Institute (ILRI), based in Kenya and their network of research partners. Your tasks will be
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very good communication and collaboration skills in an interdisciplinary and international setting. We offer: • The exciting opportunity to work in a world-class institute within an interdisciplinary and
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models and neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning