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Field
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advanced statistical machine learning, reinforcement learning, and gen-AI-driven decision models for supply chain and operations optimization. • Design scalable algorithms for demand forecasting, risk
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better and faster decisions when assessing funding applications, ensuring the efficient and unbiased elimination of poor applications? This question can be addressed through training algorithms on past
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should operate in an AI context. The AI revolution has sparked ongoing debates that highlight the multifaceted role of AI and algorithms in shaping our world—in ways that engage deeply with law. Fully
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-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter rail operations. Key research topics include eco-driving, environment cooperative perception
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of solvers for stochastic optimization problems, and test the methods on real-life data. As part of the PhD you will be following advanced courses to extend your skills, implement and test algorithms, and
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’ algorithms, however these may not provide physically interpretable results or quantifiable uncertainty. We propose developing data pipelines combining advanced preprocessing techniques, statistical tools, and
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of ne computational chemistry algorithms in efficient computer codes • Demonstrated ability to approach complex problems creatively • Experience and/or interest in mentoring undergraduate
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of peptide design and chemistry, computational methods (machine learning, deep learning, genetic algorithms), microbiology, synthetic biology, and related areas essential to developing novel
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animals, while Prof Durbin's works on computational genomics and large scale genome science, including the development of new algorithms and statistical methods to study genome evolution. Moving forward
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete