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Field
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, Cryptocurrencies and Machine Learning Why Choose Us? World-class Faculty: Learn from leading experts with publications in top-tier journals State-of-the-Art Facilities: Benefit from access to our bespoke dealing
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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are able to demonstrate that they have equivalent professional experience. See our latest eligibility criteria: collaboratoryresearchhub.ac.uk/l25-phd-eligibility-criteria What you will gain You will acquire
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computing and machine learning—unlocking new possibilities for precision metrology and non-destructive evaluation (NDE) in modern industry and healthcare. This PhD will push the boundaries of next-generation
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on combining innovative technologies such as remote monitoring, large language models, machine learning, blockchain, and eco-accounting to enhance the efficiency, security, and sustainability of e-bike charging
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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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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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Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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composites To propagate uncertainty in material behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help