79 structural-engineering "https:" "https:" "https:" "https:" "https:" "ICube laboratory CNRS" Postdoctoral positions at University of Oxford
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band structure and exciton binding energies, as well as their vibrational and transport properties. This role will utilize several state-of-the-art computational modelling techniques, in particular
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years This role will contribute directly to drug discovery efforts through the design and synthesis of small-molecule inhibitors. The postholder will use structural, biochemical and microbiological data
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design, development and execution of biochemical and biophysical assays to identify and evaluate inhibitors targeting mycobacterial proteins. Working closely with chemists, structural biologists
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We are seeking a full-time Postdoctoral Research Assistant to join the Environmental Fluid Mechanics Group at the Department of Engineering Science in central Oxford. The post is externally funded
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science, engineering, or a related discipline, with significant postdoctoral research experience. The ideal candidate will have strong expertise in computational biology, machine learning, and quantitative analysis
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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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(PI), Saiful Islam, Peter Bruce), with UCL Chemical Engineering (Dr Rhod Jervis) and 4 industrial partners that brings together expertise in battery materials synthesis and device fabrication, advanced
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the molecular structural dynamics and embedded chiral information in redox-active polycyclic aromatic hydrocarbons influence the emergence of novel photoluminescent properties. While the majority of objectives
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the next generations of technology governance leaders. It is one of the few organisations in the world to focus on the governance of AI from both technical and policy perspectives. The Postdoctoral
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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