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using multimodal approaches including advanced imaging, nano-mechanical characterisation and machine learning techniques Developing physics-informed reliability models using experimental datasets
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physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell@nottingham.ac.uk . Applications
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service, has an appetite to learn, is keen to understand and support a wide range of technology and has outstanding written and verbal communication skills? We’re looking for an enthusiastic, customer
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of course content, the creation of engaging and innovative learning experiences, and ensuring all teaching meets School and University quality standards. The post holder will also supervise dissertations
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Machine Learning (OxCSML) research group, with responsibility for leading and carrying out research pertinent to the project, as well as day-to-day management of research activities relevant to the project
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programme focused on applying AI‑driven analysis to in situ advanced microscopy of fibre‑based materials. The project aims to develop and deploy machine learning tools that extract real‑time structural and
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for translational biocatalysis, addressing critical needs in the development of sustainable biotechnologies. The programme will equip PhD students with advanced expertise in enzyme science, machine learning, enzyme
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AI, machine learning, and deep learning. This is a fixed-term position, with the potential for extension. This is a full-time post 35 hours per week, and you will be offered a fixed term contract until
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, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy
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image and video processing algorithms Developing and adapting machine learning models Integrating and deploying algorithms onto industrial hardware Evaluating performance in realistic scenarios and