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First Stage Researcher (R1) Country United Kingdom Application Deadline 9 Oct 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Part-time Hours Per Week 5 Is the job funded through the EU Research
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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
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vibrant research and development team, collaborating closely with our industry partners; Costain, National Highways, DfT and our spin-out, Didimi, and domain experts to contribute to the development of a
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, profilometry and AFM. You should also be familiar with theory of plasma discharges and have the background required to extract plasma parameters from plasma diagnostics data and with methods to perform time
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-resolved mass spectroscopy and should be versed in materials characterisation methods including XRD, nanoindentation, profilometry and AFM. You should also be familiar with theory of plasma discharges and
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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contributing positively to a collaborative research environment. Desirable: experience with building energy or power system applications, cooperative or coalitional game theory, or high-performance computing
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in genotyping, molecular biology and Drosophila work. CDN has close partnership with the Medical Research Council (MRC) and CDN researchers, together with clinical researchers from King’s, make up
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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& Neuroscience and is located at Guy’s campus. Researchers have access to support facilities in genotyping, molecular biology and Drosophila work. CDN has close partnership with the Medical Research Council (MRC