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Field
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that activity-silent mechanisms, such as short-term synaptic plasticity, also play an important role. We will experimentally target these two mechanisms, using EEG in combination with machine learning to reveal
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modeling, optimization, and/or data analytics. Description of the PhD topic (subproject C1 - New metrics for transportation system appraisal): Transportation systems must deliver “function” (i.e
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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collaborative project that spans multiple continents. You will contribute to the development of new chronobiological analytics on existing data, design experiments to collect novel chronobiological data, engage
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the molecular level. While structural predictions using deep learning methods like AlphaFold have revolutionized our understanding of sequence dependent molecular structure, we currently have much more limited
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Ability to work independently as well as in teams. Work in a structured way, set goals and make plans to achieve them, result-oriented. Excellent analytical skills, analyze data, assess different
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microscopy Experience in cell sorting via FACS and experimental lung research would be advantageous Extraordinary level of autonomy, initiative, analytical thinking and ability to work in a team Excellent
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understanding of the process, statistics and data analytics shall be applied to link the different conditions to the likelihood of microcracks occurring. The severity of microcracks may also be studied in
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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landscapes including the UK, focusing on challenges that emerge at the intersection of hazard exposure and health service access and availability. Potential projects should involve multi-data analytics (e.g