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to compliance professionals for validation studies. Training spans advanced NLP (transformer fine-tuning), financial crime typologies, privacy-preserving machine learning, and product-oriented development. You
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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good collaborative skills within a team. You may also have strong theoretical and programming background in AI, machine learning, mathematical modelling including physics-based models, and/or robot
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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
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, registers and other clinical studies Knowledge and practical application of various statistical packages, such as R and Python Ability to adapt and/or develop statistical and machine learning methodology
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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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machine learning and experimental design optimisation. Training and Career Development You will gain interdisciplinary skills in machine learning, medical image analysis, and coating design. As a PhD
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machine learning with spectral data to enable rapid, non-destructive detection of food adulteration and fraud. Machine learning combined with spectral data can play a vital role in combating food fraud by