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. Engage in professional development through relevant training programs aligned with project needs and personal aspirations. Assist with the supervision of student project work and provide day-to-day support
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the lab existing genome sequence data for fungal plant pathogens within the Dothideomycete family. The lab has an existing fungal collection of over 40 strains collected from wheat, wild-grasses and barley
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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on the molecular mechanism of DNA replication initiation in bacteria. This work is aimed at unravelling the sequence of events that lead to helicase loading during initiation and restart. This structural biology
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from home and the office. However, as this role is contractually aligned to our Milton Keynes office it is expected that some attendance in the office will be required when necessary and in response
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rigorous, collaborative research aligned with project goals. Develop and apply deep learning models, particularly in computer vision, NLP, and multimodal systems. Publish in peer-reviewed journals and
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(notably MCP, A2A). Note that the successful candidate will work in collaboration with two other postdocs on a closely aligned project “Rethinking multi-agent systems in the era of LLMs”, funded by
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successful candidate will work in collaboration with two other postdocs on a closely aligned project “Rethinking multi-agent systems in the era of LLMs”, funded by the Schmidt Sciences Foundation, and also
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will be the analysis of virus sequence data generated through our surveillance activities. Surveillance will be based on next-generation sequencing of viral RNA/DNA extracted from wastewater samples. We
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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform