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electric machines for future defence and aerospace propulsion systems. The research will explore innovative machine topologies and advanced multi-physics design methodologies to enable electric machines with
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university. We are seeking a full-time Research Assistant (“pre-doc”) to work closely with Professor Neil Lee on a new funded research project examining whether public green lending crowds in private capital
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and advanced machine learning. The project will integrate measurements from the SWOT satellite mission with Oxford's Global River Topology (GRIT) hydrography to develop verified, uncertainty-aware
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, classics, gender studies, political science, and international studies—with cutting-edge data science techniques, including Earth Observation (EO) data analysis, machine learning, large-scale collation and
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will be responsible for the follow : (full details of duties available from the Job Description) Research Collaboration and engagement You will have completed a PhD in machine learning, computer science
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. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role. Candidate requirements Candidates must have expertise in developing computer vision and machine learning
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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Fully Funded PhD Studentship (UK Students Only) Real-Time Sub-THz Electromagnetic Sensing and Machine Learning for Dynamic Particulate Characterization University of Birmingham with support from
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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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evidence to support the evaluation of AI and machine learning models. This may include investigating data-centric AI strategies, such as data quality assessment, annotation refinement, dataset curation, and