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. Programming experience (e.g. Python, MATLAB, C/C++). Desirable (but not required): Background in control theory, dynamical systems, optimisation, or machine learning. Experience with robotics, ROS, simulation
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Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after
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appraisal, to ensure that design choices do not undermine the sustainability imperatives driving the transition to green-fuel propulsion in the first place. Research Aims This PhD programme addresses
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our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme
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Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career
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skills with understanding of engineering design principles. Any experiences with CAD/CAM systems and programming are preferred. Funding Support The University promotes equality, diversity, and inclusion
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foundation in either machine learning or mathematical/computational neuroscience, demonstrable programming experience (Python/PyTorch), and the curiosity to work across disciplinary boundaries. A background in
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benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions
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with substantial research experience will be highly advantageous. Experience in computer programming will be essential. Studentships are open to home students only. The deadline to have completed and
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Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within