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funded by UKRI and is fixed-term to 31 December 2026. This is an on-site position only. The project aims to develop a computational model for biodegradable polymers degrading in water under mechanical
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to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
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. You must have demonstrated experience in in in vivo models of inflammatory disease and a flexible approach to dealing with research problems as they arise. You must demonstrate excellent communication
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the commercialisation of all-solid-state batteries. Of particular interest is the development of electro-chemo-mechanical phase field models to predict void evolution and dendrite growth (see, e.g., doi.org/10.1016
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developing the theoretical and algorithmic foundations of compositional world models. A key application focus of the grant lies in rapid and safe real-world skill acquisition in application domains such as
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in immunology or a related field. You should demonstrate proficiency in innate and adaptive immune cell assays such as flow cytometry and ELISA, and have proven experience in in vivo models
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partial drainage effects. You will contribute to the numerical modelling part of the project, which will benefit from novel element level and centrifuge testing experimental results. You will set up and
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
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the human microbiome with proficiency in laboratory-based immunology techniques, such as flow cytometry and ELISA. You must have demonstrated experience of in vivo models of inflammatory disease and a
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disease. Your research may utilise a range of approaches, including targeted genetic murine models, primary cell culture and analysis, multi-omics, and bioinformatics. The biological focus will be