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Field
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experiments that will reveal the physical mechanisms of transport and burial of microplastics and POC in turbidity currents down submarine canyons; incorporate these mechanisms into an existing computer model
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–Science research-creation project bringing together engineering, life sciences and design. This interdisciplinary framework will enable the candidate to develop innovative experimental models while engaging
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Fully Funded PhD Studentship (Home Fees) Project Title: Developing Fair and Robust Cancer Risk Prediction Models Using Health Data Supervisor: Professor Angela Wood (Professor of Biostatistics and
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). This exciting translational role sits at the intersection of innovative stem cell engineering and disease modelling, targeting genetic, metabolic, and immune-driven pathologies. You will utilise proven kidney
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down submarine canyons; incorporate these mechanisms into an existing computer model for sediment flux through submarine canyons; and validate the model on monitored submarine canyons. The responsibility
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develops and uses ultra-fast and ultra-intense lasers and laser-driven short-pulse light sources in a broad spectral range in combination with methods of nonlinear spectroscopy. With its research, MBI
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. This includes building model-ready databases, developing simulation chains that link process simulations with analysis, making workflows for validation, and contributing to developing AI-driven tools
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traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where possible. This innovative new approach enables more efficient and
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combining technology development with process modelling and assessment across the waste value chain. The members of the consortium include 4 other research Universities, 2 Universities of Applied Sciences
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Requirements We are seeking an enthusiastic and results-driven candidate who meets the following criteria: A successfully completed MSc degree in: Agricultural economics, food and agribusiness, or sustainable