490 web-developer-"https:"-"https:"-"https:"-"Institut-Agro-Rennes-Angers" Fellowship positions in United Kingdom
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develop foundational security theory for agentic and multi-agent AI systems, as part of our research programme in multi-agent security. You will build formal models of security-relevant behaviour in
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Obstetricians at Liverpool Women¿s Hospital with broad ranging interests in all aspects of obstetric care from development of new technologies through to randomised trials of complex interventions. The successful
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autoantibodies in autoimmune disease pathology, to reveal the underlying mechanisms by which anti-MICL autoantibodies mediate their pathological effects and develop approaches to block the effect
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. The group will include diverse health professionals focusing on Asset-Based Community Development approaches. The long-term vision is to establish a Centre of Excellence for mental health asset-based
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and to integrate these lines into the drug-development pipeline to aid the discovery of novel therapeutics that eliminate “persister” parasites. This will involve undertaking research on Trypanosoma
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conferences. This position offers an excellent opportunity to further develop research independence in a dynamic, multidisciplinary environment, with the scope to develop novel modelling tools and experimental
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work on the development and implementation of methods for improving small area modelled human population estimates. The successful candidate is expected to have strong quantitative skills and experience
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zebrafish as a primary model system for our work, where the developing heart can be visualised in living animals in real time. Please visit our website to find out more about Toby's research. About the role
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Dominick Shaw on des21@le.ac.uk As part of the University’s ongoing commitment to professional development, this role will also be considered on a seconded basis for existing staff members. Please ensure
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the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward