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Adopter programmes, where appropriate. We are looking for an enthusiastic, motivated graduate in (health) Data Science, Computer Science or related field who is interested in the deployment, user acceptance
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Neutron total scattering (Pair Distribution Function analysis), as well as scanning and transmission electron microscopy (SEM & TEM). Main duties and responsibilities Undertake the programme of work and
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. (assessed at: application & interview) Desirable criteria Knowledge of computer programming with experience in a scientific program language e.g., Python, Java, C++, C, C#, LabVIEW, MATLAB, Halcon, R, Maple
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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, transcriptomics arrays and serological assays will be exploited to investigate biomarkers in cultured cells (primary supervisor). Field work at the Malawi Liverpool Wellcome Trust Clinical Research Programme will
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facing the UK’s programme of remediation and restoration of the Sellafield site, expected to be complete in the next century. The PUMaS hub will support ~20 PhD students along with research staff, creating
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Engineering, Manufacturing, Materials Science, or a related discipline A strong interest in experimental research and advanced manufacturing technologies Desirable but not essential: experience in additive
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Engineering, Manufacturing, Materials Science, or a related discipline A strong interest in experimental research and advanced manufacturing technologies Desirable but not essential: experience in additive
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reliable, non-contact, emissivity-independent temperature imaging and machine vision. The work contributes directly to several areas of science and engineering, including robotics and remote maintenance
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collaborative programme of research funded by the Aerospace Technology Institute (ATI) with several Industry partners, including Airbus, GKN and Renishaw. Critical for the implementation of additive manufacturing