76 structures-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"LGEF" positions at University of Oxford
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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
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-High and Extreme-High Vacuum environments. A growing focus of the role is the design and analysis of lightweight composite structures, particularly ultralight carbon-fibre systems for advanced physics
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will operate with a high degree of autonomy, shaping campaign structure, optimisation approaches and budget allocation to maximise ROI and support recruitment objectives, while providing expert guidance
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the molecular architecture of the chloroplast’s beta-barrel protein assembly machinery using structural tools. The successful candidate will have a PhD with relevant experience in protein biochemistry and
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-wide AI network, develop a coherent external presence, and create the structures that help researchers collaborate more effectively. You’ll shape how Oxford presents its AI work to funders, policymakers
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have advanced knowledge of German and good reading knowledge of at least one other Eastern European language, particularly Greek or a Balkan language. You will bring experience of structured data entry
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thin-films, and identify how manipulating these affect the nanoscale chemical and structural composition of perovskite thin-films. Through gaining understanding and control of these systems from
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. This includes working with contractors and scientific users during the setup phase, making sure equipment, documentation, and safety systems are in place, and helping transition the space from construction
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with a wide range of stakeholders and producing clear, well-structured written outputs. You will have: Proven analytical and problem-solving skills, gained through a good honours degree or relevant
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traditional ecosystem assumptions (e.g., reliable institutions, credible actors, and enabling infrastructure) do not hold. The project is structured around two in-depth, qualitative case studies (Affinity