86 modelling-and-simulation-of-combustion-postdoc Postdoctoral positions at University of Oxford
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record in studying humans and machine learning models, in the context of human social behaviour, learning, decision-making, or a related area. A proven track record of publishing work as lead author in
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, activation, and effector functions in preclinical models of autoimmunity. This research is part of a broader effort to define how inhibitory receptors tune T-cell responses in health and disease, ultimately
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). The post is funded by NIHR and is fixed-term for 24 months, with a possible extension. This project is about creating novel AI models to predict patient outcomes following acceptance or refusal of an offer
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We are seeking to appoint a highly motivated Postdoctoral Researcher with expertise in innate immune responses to cancer, in vivo/in vitro experimental models, and advanced molecular techniques
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will have three responsibilities: - (1) studies of composite materials for a beam-facing RF shield. (2) contributions to the operation and exploitation of the Timepix4 testbeam. (3) simulation work
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vitro and cell-based approaches. In this endeavour the position is for a Postdoc with expertise in cell signalling pathways, protein biochemistry and in vitro cell biology. You will be responsible
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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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this hydrogen generation model with the ammonia synthesis module. Find out more about the Hayward research and group at: https://www.chem.ox.ac.uk/people/mike-hayward. About you Applicants must hold a
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore