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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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seek a proactive and enthusiastic individual with a first-class or upper second-class honours degree (or equivalent) in: Engineering (Mechanical, Materials, or related disciplines) Physics, Chemistry, or
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
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may be possible, please contact Dr Mark Whiting once deadline passes. You will need to meet the minimum entry requirements for our Engineering Materials PhD programme. Candidates must meet Surrey
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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Are you interested in developing computational tools to understand the detailed mechanical behaviour of multi-phase materials? Then this PhD position at Chalmers University of Technology might be
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Mineral Residues: Comprehensive Characterization and Advanced Material Applications Hosting: Tallinn University of Technology, secondment: The University of Eastern Finland (FI) Supervisory team: Dr. Birgit