168 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at University of Oxford
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. To address these questions, we combine a series of interdisciplinary approaches ranging from experimental embryology and fluorescent microscopy to mathematical modelling. The lab is highly interdisciplinary
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
, epidemiology, and socio-environmental modelling. To be considered a successful candidate; A PhD degree in Ecology, Biodiversity analyses, Environmental Science, Remote Sensing, Epidemiology, Data Science, or a
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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, Oxford, Leeds, Reading, and Birmingham) and international (Utrecht University, ETH Zurich, Université Catholique de Louvain, etc.) scientists to use new modelling resources and methods to elucidate drivers
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systems modelling including technical knowledge (e.g., in data science, input-output modelling, applied economic modelling, environmental and ecological assessments, GIS, comparative risk assessments), as
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Institute). The position is fixed term for 36 months and will provide opportunities to work on aircraft icing modelling and experimental campaigns. Ice crystal icing is one of the least well characterised
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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an available option. Applicants with a range of academic subject backgrounds are welcomed, including natural sciences, epidemiology, engineering, statistics and applied mathematics with experience and
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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, and enabling data-driven improvements in patient care. You will have opportunities to apply foundation models—including large language models (LLMs) to real-world clinical data. You will work with well