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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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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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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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computational modelling techniques, including recurrent and generative models. We are also in the process of developing different toolboxes to make these techniques more available across both our group and
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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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operational efficiency. Led by Professor Chris Holmes, the centre will initially focus on the following thematic areas: Decision analysis under model misspecification Uncertainty quantification around LLMs
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statistically reliable AI, with applications to engineering. The research will focus on the design of formal reliability guarantees for black-box AI models operating under highly non-deterministic and context