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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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(e.g., DFT modelling), or solid-state NMR spectroscopy. Excellent communication skills, a collaborative approach, and the ability to work independently are also key requirements for this position. A
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(e.g., DFT modelling), or solid-state NMR spectroscopy. Excellent communication skills, a collaborative approach, and the ability to work independently are also key requirements for this position. A
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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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collaboration between scientists in the MRC PPU and signalling researchers at the University of Dundee’s School of Life Sciences and the pharmaceutical industry. The DSTT is widely regarded as a model for how
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of mole activity and soil health and biodiversity, collecting data on visitor perceptions of moles and their management, and analysing findings using statistical modelling approaches. The role provides
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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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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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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