215 structural-engineering-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" Postdoctoral positions in United Kingdom
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, semi-structured interviews and empirical research in diverse settings. They will be confident managing sensitive data, maintaining rigorous and well-organised research records, and working in accordance
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This is a fixed term externally funded role for 18 months offered at 0.9 fte. The role starts 1st July 2026. The role Based within the Faculty of Health, Science and Technology and reporting
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A National Institute of Health and Care Research (NIHR) Health Technology Assessment funded Postdoctoral Research Associate (PDRA) position is available at the Department of Cardiovascular and
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research and teaching in the fields of marine evolutionary genomics and climate change adaptation. University-internal structures and programs, such as the Vienna Doctoral School of Ecology & Evolution
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to disrupt cancer-relevant protein–protein interactions. The project will focus on the design, synthesis, and biological evaluation of structurally constrained peptide macrocycles that access bRo5 chemical
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. The group investigates both the fundamental properties of these proteins and their applications in biotechnology. A long-standing focus of the laboratory is the engineering of protein nanopores
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evaluate inhibitors targeting mycobacterial proteins. Working closely with chemists, structural biologists, microbiologists and immunologists at Oxford, as well as with external partners, the postholder will
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-molecule inhibitors. The postholder will use structural, biochemical and microbiological data to inform molecule design, generate and test hypotheses, and develop new synthetic methodologies. Alongside
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technical direction of the project. About You Applicants must hold a PhD (or equivalent research experience) in engineering, chemistry, physics, materials science, or a related discipline. Some of the
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to work within one of, or across, the four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning