169 phd-mathematical-modelling-population-modelling Postdoctoral positions at University of Oxford
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lowland river chemistry. Ultimately, these experiments will be used to parameterise calcite precipitation rate equations and empirical rate constants to inform catchment-scale modelling of ERW practices and
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methods to study human bone marrow models using high content imaging approaches. You will lead in designing and establishing new protocols to the laboratory as well as supporting, mentoring and training
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Economics Research Centre (HERC), Nuffield Department of Population Health, with the opportunity to also contribute to HERC’s varied programme of teaching. You will manage your own academic research and
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, operations research, computer science, mathematical finance, or a related field, the successful candidate will demonstrate the ability to develop independent research ideas and contribute to advancing our
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projects with colleagues in partner institutions, and research groups. You must hold a PhD/DPhil (or near completion). You will have extensive experience in live imaging of the spleen using 2-photon
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems
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on process development, electrode manufacture and performance assessment, but depending on the skills of the successful applicant, may also involve some aspects of modelling or data science. The post is funded
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evaluations, attacks on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related
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data to build hypothesis and test them in laboratory models. You will contribute ideas for new research projects, collaborate in the preparation of scientific reports and journal articles and act as a
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Prof. Luigi Rizzi (Collège de France), seeks to investigate the acquisition of French from a cartographic perspective, employing the Growing Trees model developed by Friedmann, Belletti, and Rizzi, and