117 advance-soil-structure-modeling Postdoctoral research jobs at University of Oxford
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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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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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
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extensive knowledge and experience in DNA damage response field, cancer biology, or mechanobiology. Possess technical expertise in advanced molecular biology techniques such as CRISPR, RNAi, DNA manipulation
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tomato and pepper as model systems. Work in Oxford will build on our extensive experience in studying bacterial virulence mechanisms and the role of the plant microenvironment in disease development
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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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containing information on various dimensions of social and economic development and inequality. The Postdoctoral Researcher will be expected to engage in advanced independent research within the remit
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Project Overview This 5-year BBSRC supported project is a highly multi-disciplinary effort between biology and engineering groups at the University of Oxford to advance cell-based assays
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of advanced X-ray methods to explore chemical, crystallographic and morphological changes that drive battery performance loss. You should possess a doctorate in a relevant engineering or physical science
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scientific oversight from Oxford Principal Investigators and GSK scientists, the centre will initially focus on some of the following thematic areas: • Decision analysis under model misspecification