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degree or equivalent in a related discipline. This project would suit individuals from a variety of backgrounds such as machine learning, statistics, and economics. Candidates with experience or keen
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offers a modular, cohort-based training programme with emphasis on innovation and impact, collaborative working and learning, continuous development, active engagement with partners and stakeholders and
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learning from in-service vehicle fleets and predicting remaining useful life. Applications of artificial intelligence and computer science to battery state estimation. Reduced-authority control of hybrid
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robust intention prediction machine learning algorithms such as inverse reinforcement learning. Using synthetic and real radar data to solve real problems. Developing the evidence base to inform partners
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in autonomous systems, advanced perception, and human-machine interface design that augments human cognition. Candidates will have the opportunity to contribute to cutting-edge developments in
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science and machine learning. Our Values and Commitments Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here . We
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ecosystems. This PhD project will contribute on the targets mentioned above by combining organic chemistry, aerosol science, computing, engineering and machine learning for adapting the existing CRIM-TRACK
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; biological science; biotechnology; data science; ecology, entomology, environmental chemistry and geography; food production and science; genomics; livestock farming, health and welfare; machine learning
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, these systems serve as complex functional approximators trained over an input-output data set. ‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute