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The Institute for Energy Systems at the School of Engineering of the University of Edinburgh is looking for enthusiastic, self-motivated applicants for an exciting PhD position that will research
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations
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exhaustive, and the PhD candidate will be required to undertake such tasks and responsibilities as may reasonably be expected of a PhD candidate. Application process The application should include: a
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several processing units with variable memory, can be profiled to pool the resources. The analytical systems, developed on data collected by onboard sensors and software triggers, can assist the operating
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to remove PFAS from water sources used for drinking. Part of the planning process involves evaluating operational aspects of these treatment processes such as contact time, media type and regeneration
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atomisers. To ensure high quality measurement data, enabling meaningful correlations, measurements will be performed on a simplified prefilming geometry, with aero and hydrodynamic features representative
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for their projects. AI transformation processes in projects and organizations Ethical considerations of using AI in project planning and delivery The use of project data analytics in AI-driven decision-making Machine
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will enhance the understanding of physical mechanisms affecting the processes of pollutant dispersion in the wake of passenger cars. This is a double-PhD study and so the student will spend the first 18
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Start date: November 2025 This PhD is an exciting opportunity to expand knowledge and understanding in the applications of natural chitinous polymers for bacterial/viral prevention and biofilm
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computer literacy, good inter-personal communications skills. Desirable skills: A Master in Health Economics with experience in cost effective analyses. Funding notes The three year studentship covers UK