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. For modeling, we use both public and proprietary clinical and research data and generate our own repository of digital pathology images. A further focus of our lab is the improvement of digital pathology
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hearing loss. However, current neural devices are large, complex, and invasive, and are therefore used by only a fraction of people who could benefit from them. The goal of NANeurO is to design new
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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
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detail: Development of APIs for electrolysis systems and analysis devices Implementation of autonomous process control Conceptualization and implementation of degradation models for electrolysis Studying
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, depending on the geographical and economic context. It will include a deep dive on the potential of Ukraine to become a green hydrogen hub, leveraging geo-spatial energy models run by project partners. As
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methods, machine learning algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems
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, control, effective models and their numerics “. Here, we study anisotropic microfluids and the effect of stochastic fluctuations in electrokinetic flows. This is of interest in so-called lab-on-chip devices
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management. Our group combines empirical work (with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets
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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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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