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2019, unites top PhD students in all areas of data-driven research and technology, including scalable storage, stream processing, data cleaning, machine learning and deep learning, text processing, data
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is currently the main focus. Here, laboratory experiments are usually combined with state-of-the-art methods such as optogenetics, connectomics or machine learning. Activate map To activate the map
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defects of smectic-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
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parameter space of the electrolysis processes. DoE is required for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map
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: process multisource satellite and UAV based data collected in the case study regions apply and develop models for tillage mapping and monitoring using remote sensing apply and further develop machine
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data engineering. The research will focus on data preparation and data pipelines for complex machine learning (ML) systems. Such ML systems are increasingly used to automate impactful decisions but
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platform for cancer, in collaboration with experimental partners. Your tasks: Development and application of interpretable large-scale hybrid mechanistic- and machine learning-based mathematical models with
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motivated colleagues who are passionate about machine learning, optimization, and game theory. - an attractive work environment both within the research group and beyond. For further information concerning
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different sources (observation, model output) You are interested in applying methods for automated detections of weather (e.g., cyclones, jets, frontal systems) which can include machine learning methods You
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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal