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Requirements We are seeking enthusiastic, curious, and motivated individuals with: A strong academic background in computer science, artificial intelligence, machine learning, data science, engineering, or a
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Location: Central Cambridge PhD Studentship - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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Understanding plankton biodiversity and ecosystem change by applying machine learning – A CASE studentship Lead Supervisor (DoS): Professor Abigail McQuatters-Gollop Second Supervisor: Dr Clare
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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, please visit our website at www.cruk.cam.ac.uk/research-groups/aliee-group In the Aliee lab, we aim to address some fundamental questions in biomedicine through advancing machine learning. We develop
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust