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cardiovascular disease progression and outcomes. The successful candidate will work to identify key clinical and immunological predictors, develop risk models, and help generate new hypotheses to inform future
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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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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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, penetration and toxicity of novel metallodrug-coated gold nanoparticles in a 3D-cell culture model of head and neck cancer. This is a highly inter-disciplinary project involving collaboration between Dr Nik
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This role is an exciting opportunity to work on the interplay between biology and physics on ocean carbon sequestration by the Biological Carbon Pump. You will run an ocean biogeochemical model
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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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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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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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-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