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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
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random
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(with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets for livestock systems in East Africa, and in
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of immune cells and in functional assays determining T cell functions - Experience with in vivo experiments in preclinical model systems of viral infection - Excellent writing and communication skills
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qualifications. Full-time employment is envisaged; models that enable continued employment at your home institution on a small scale are possible. The minimum duration of stay in Munich is 18 months, the maximum
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materials science • Extensive knowledge of computer-based modelling and simulation methods in materials science of metals, e. g. Calphad method, precipitation simulation, cellular automata, kinetic Monte