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will be able to play a leading role in developing Israel Studies at the University of Oxford. The successful candidate will be given the opportunity to launch their post-doctoral career. They will have a
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fixed term contract for 36 months. Research staff at King’s are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career
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and care organisations throughout the region. The postholder will work on developing projects and a community of practice aiming to support women’s mental health in underserved communities. The role
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. These postdoctoral positions are designed to support the career development of the post holders. Alongside contributing to the work of the project, it is expected that the PDRAs will pursue their own research related
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for Artificial Intelligence, and the Digital Futures Institute. The PDRA will also have access to research and career development from King’s Centre for Research Staff Development. The postholder will work closely
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to attract, develop, and retain colleagues with diverse backgrounds and perspectives. We welcome applications from all genders/gender identities, Black, Asian, or Minority Ethnic backgrounds, individuals
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Location: Life and Mind Building, South Parks Road, Oxford OX1 3EL About the role We are seeking a Postdoctoral Research Associate to support our projects to understand membrane evolution. The aim
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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expected to undertake and/or support interviews and qualitative analysis and will be involved in data collection from the museum collections, and all analysis, and publication development and conference
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the Finnish Center of Excellence in Quantum Materials . Your role and goals The research will focus on developing and using machine learning algorithms to discover novel materials and to build generative models