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The funding provider: Health Data Research UK Subject areas: Wider Data Science Field, Maths and Computer Science Project start dates: 1st October 2025 ** (Please see the note below regarding
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parameters/mechanisms and model outcomes by varying the parameters simultaneously over a large range of values. Once the simulations are performed, the output variability can be apportioned onto the input
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collaborative studentship between the University of Edinburgh and the National Quantum Computing Centre (https://www.nqcc.ac.uk ). The position will be registered and hosted at the University of Edinburgh and
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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
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PhD programme within QMUL’s Wolfson Institute of Public Health, under the supervision of Dr Giuliano Russo (WIPH) and Prof. Pietro Panzarasa (School of Business and Management). As part of the project
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Project description: Reliable, high-resolution electricity demand data are scarce across the global south, limiting our understanding in these regions of the relationship between weather and energy demand
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and linear steep sided cut channels, which can restrict creek development and negatively impact site functioning. This project will use remote sensing, geophysical surveying and sedimentological
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isolation, leading to suboptimal network performance. This project seeks to address this gap by developing a holistic, system-level approach that optimises PST deployment strategies to enhance grid
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of measles evolution and naming systems (nomenclature) to support public health identification of measles outbreaks/importation and ultimately, elimination. In this project you will learn to perform genomic
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significantly reduce carbon emissions and bolster global efforts to achieve net-zero targets. Despite considerable advancement in their operational performance, their life cycle impacts, including raw material