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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while enabling more
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of complex molecules and reactions. Must have PhD in Physical/Theoretical Chemistry or Artificial Intelligence/Machine Learning or related fields. This position will be initially awarded for one year, and
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spatial and temporal variability in sediment accumulation and vegetation development. This PhD project is part of a larger interdisciplinary research initiative aiming to enable a transition towards more
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twins, optimization, and control. In this PhD project, you will develop a new systems and control theory for learned operators, bridging modern scientific machine learning with classical control theory
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player who contributes positively to collaboration and project success. You also possess: a PhD in Artificial Intelligence, Machine Learning, Computer Science or a related field; at least 3 years of hands
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analysis, and data visualization. Apply advanced statistical, machine learning and AI methods when appropriate. Review analysis outputs and ensure methodological consistency and quality. Collaborate with
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas