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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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experiments and policy-capturing methods may be used to compare interviewer judgements with evidence-based outcome measures. The project will also explore machine-learning, multimodal data analysis, computer
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workflows to implement such interactions into supramolecular hosts (pubs.rsc.org/en/content/articlelanding/2022/cc/d2cc00532h ). You will learn a wide range of molecular modelling and data-driven techniques
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Honours degree in biology, genetics, biomedicine, or a related area. The candidate must be willing to learn bioinformatics. A further qualification such as an MRes is advantageous. The studentship covers
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, biomedical engineering, advanced image processing and machine learning. The studentship suits a candidate with a strong background in optometry, physics, engineering, computer science or a related discipline
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic
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would also be welcome. While the post is primarily focused on teaching and learning, the School actively supports scholarship and research activity. The successful candidate will be encouraged to develop
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structures may change with time, where the change can depend on latent factors or variables. These projects will focus on developing a comprehensive Bayesian learning framework for this broad class of problems
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(or equivalent) to model neutron transport and tritium breeding behaviour within breeder blanket configurations. The project will then extend toward accelerated predictive methodologies using machine learning and