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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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control engineering, optimization algorithms Control of drones and flight experiments as well as knowledge in AI / Machine Learning would be an asset Outstanding academic records Teamworking experience, e.g
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, data scientific or machine learning background that is keen to work in an interdisciplinary environment and open to collaborating with researchers from other disciplines. The successful candidate will
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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Kolter, Tjibbe Donker and Philipp Henneke analysis of multiscale single cell ‘omics data in in experimental and human models reference genome and transcriptome assembly and annotation across species
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defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and enable charge
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of the project is to use machine-learning assisted molecular dynamics simulations incorporating quantum effects for the identification of new variant-specific drug targets which will be validated experimentally
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willingness to learn and apply machine learning approaches We offer A versatile and challenging job in a vibrant and world-class research environment operating at an international levelParticipation in a large
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-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this project, we highly