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particle simulation methods, validated by experiments, to develop a scaling method to predict continuous blending performance based on lab scale mixing performance, process parameters and physiochemical
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or master’s course with substantial mathematical content, e.g. mathematics, statistics, physics, management science, or computer science. Early applications are encouraged, as candidates will be invited
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a first or upper second (2.1) class Master's degree (or equivalent) in chemistry, physics, materials science, computer science or other related discipline. Candidates with strong BSc (Hons) degrees in
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sensing (e.g., PlanetScope, Sentinel-1), advanced numerical modelling (HEC-RAS, Delft-FM), and targeted field surveys to map mining intensity, simulate channel adjustment, and assess changing flood hazards
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2:1 in BSc Chemistry or an MSc in any applied chemistry degree, including inorganic chemistry, chemical physics, analytical methods, simulation and modelling of chemical reactions. English language
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applicant must have (or be close to obtaining) a relevant PhD in Fluid Mechanics from an Engineering, Mathematics or Physics Department, a strong background in theoretical and computational fluid mechanics
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critical, to ensure expected engine performance is achieved. To predict this complex flow and heat transfer, next-generation Computational Fluid Dynamics (CFD) solvers using Large-Eddy Simulation (LES) and
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. This project will combine numerical simulations, field observations, and stakeholder engagement to address three core research questions: 1. How does sediment transport reduce the efficiency and resilience
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a relevant subject (physics, mathematics, engineering, computer science, or related subject) Proficiency in English (both oral and written) A strong background in computer science, artificial
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offers a non-intrusive, low-cost, and privacy-preserving solution. The research will involve designing and testing experimental setups, collecting vibration data from simulated falls and everyday impacts