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Field
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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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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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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 project will develop machine learning based solutions for predictive grid analytics (such as grid congestion forecast, asset monitoring, etc.). Based on these results, the project will develop
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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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Operations Management Department invites candidates to apply for a 2-year postdoctoral position. We are seeking a highly motivated Postdoctoral Researcher to join our team working on Statistical Learning and
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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themselves to analytical treatment; Writing up results and publishing them in reputable international journals. As a PhD student, you devote most of your time to doctoral studies and the research projects
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address