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term role to support a large project looking at developing outdoor thermal comfort models for children? If so, we would welcome your application to this full-time, fixed term role. As a Post-doc Research
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In Vitro Predictive Models to Explore Tendinopathy”. The project is funded by the Medical Research Council (MRC) and part of the organ-chip research work underway within the Centre for Predictive in
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Institute). The position is fixed term for 36 months and will provide opportunities to work on aircraft icing modelling and experimental campaigns. Ice crystal icing is one of the least well characterised
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form, how it is generated and how it evolves. In particular we focus on the evolution and evolvability of vertebral counts, and we use various species of Lake Malawi cichlids as our model organism
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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, Oxford, Leeds, Reading, and Birmingham) and international (Utrecht University, ETH Zurich, Université Catholique de Louvain, etc.) scientists to use new modelling resources and methods to elucidate drivers
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of the microbiome in olfaction using mouse models. The selected applicant will join the vibrant and friendly Tucker lab and work as part of a team interacting with the group of Prof Mike Curtis. The Tucker and Curtis
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the cellular and molecular mechanisms driving tumour development. In this role, you will support a team of scientists using genetically engineered mouse models (GEMMs) and transplantable tumour models (e.g
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Professor Chris Russell. This is an exciting opportunity for you to work at the cutting edge of AI, contributing to a major shift in how we understand and apply foundation models. The position is full-time
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, and enabling data-driven improvements in patient care. You will have opportunities to apply foundation models—including large language models (LLMs) to real-world clinical data. You will work with well