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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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, and potential across contexts. Embedding Artificial Intelligence and assistive technology, we aim to be at the forefront of equipping key stakeholders to identify, explore, and scaffold opportunities
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Artificial Intelligence and assistive technology, we aim to be at the forefront of equipping key stakeholders to identify, explore, and scaffold opportunities for strengths-based approaches that allow
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
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group : here About you Applicants must hold a PhD Inorganic Materials Chemistry or a related area (or be close to completion), prior to taking up the appointment. The research requires experience in
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and Mind Building, South Parks Road, Oxford Applicants must hold a PhD in Microbiology and/or Molecular biology and will be responsible for providing microbiological data to facilitate the design of new
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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 conferences, and winning further funding to underpin the research. For more information about working at the OMI, see https://oxford-man.ox.ac.uk/ . With a strong foundation in economics, finance
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and science exploitation of the MIGHTEE survey data. The postholder would have the opportunity to identify new discoveries in the data and would be ideally placed to lead the science based on the data