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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy
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Project Supervisor - Dr Stefano Landini The rapid evolution of electric vehicles (EVs), data centers, and high-performance energy storage is driving the need for battery systems that manage heat
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computing. This project is ideal for students with a background in physics, materials science, electrical engineering, or a related discipline. Experience in optics and/or magnetism is desirable but not
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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control strategies integrating fuel, engine, electric machine, and energy recovery systems for improved overall efficiency. Validate the developed methods through experimental and simulation studies
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Postgraduate Application’. Use ‘Course Search’ to identify your programme of study: search for the ‘Course Title’ using the programme code:8839F select PhD Physics Experimental (Full Time) as the programme of
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methodologies to study tyre–road contact physics. Providing new insights to better predict tyre–road friction. Based within both the Wolfson School of Mechanical, Electrical & Manufacturing Engineering and the
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Project Description: This EPSRC-funded PhD project will investigate how next-generation electric and autonomous vehicles can operate as symbiotic agents within the urban ecosystem—intelligently
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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uncertainty in meteorological forecasting, contrail process modelling, and satellite observations to quantify the skill of impact forecasts. PhD (or equivalent) in either aerospace engineering, software