594 postdoctoral-image-processing-in-computer-science-"EPIC" positions at University of Sheffield
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, protein chemistry, imaging tools (e.g., mito-pHfluorin probe) and procedures, cell survival and death assays as well as model cell lines (WT and FIS1 knockout (KO) MEFs and WT and FIS1 KO HeLa cells
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Next-generation low-carbon cement wasteforms for safe disposal of radioactive waste School of Chemical, Materials and Biological Engineering PhD Research Project Directly Funded Students Worldwide
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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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when necessary across all engineering disciplines; as well as the ability to manage workload and complete jobs in a timely manner as required. At all times work in compliance with the University’s
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round Details A central question in the study of regenerative biology is how cells are recruited to sites of injury to allow for tissue regeneration to take place. To shed light on this question, have
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techniques, to support predictive, process and product engineering. Modelling tools to be used will vary according to the application but are likely to including process simulation using Population Balance
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of the heart, image analysis, molecular biology, transcriptomics, and RNA-seq analysis. For more information about the project, or to discuss a potential application, please contact Dr Emily Noël (e.s.noel
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for low energy nuclear recoil signals for WIMP dark matter, and also contribute to the maintenance, operation and calibration of the experiment. Experience of estimation and modelling of backgrounds to low
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facilities. The CDT-GIF has an exciting and challenging programme specifically designed for top performing junior researchers. Alongside the four-year research project, students will receive expert training
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse