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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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individuals. iPSC “Village” systems and CRISPR perturbation to experimentally dissect and validate gene function in controlled, scalable cellular models. Advanced computational genomics, machine learning, and
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, machine learning, and causal inference frameworks that link genetic variants to cellular mechanisms and therapeutic opportunities. Our research spans immune biology, cardiac disease, neurodegeneration, and
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related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or Econometrics (particularly applied micro, behavioural, or decision-focused modelling) Applied Mathematics