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Field
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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receive guidance from a mentor and will learn about conducting research analysis within OP. You will engage with current OP staff on policy analysis projects and assignments in one of the following focus
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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
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on how collaborative practices evolve with these powerful tools, and support learning in disciplinary or interdisciplinary contexts. In addition, the nature of the interaction between human and machine
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wastewater treatment plants, with the following main objectives: 1 - Model calibration through Machine Learning methodologies using process data. 2 - Development of a multimodal online sustainability
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UKESM1 or similar models, advanced data analysis and machine learning, would be advantageous. Grade E: You will be near completion of a relevant PhD or have equivalent research experience, and be able
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field of engineering Research experience in one or more of the following: geometry, optimization, dynamical systems, mechanics, probability and statistics, data science, machine learning Evidence of
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effects and their coupled interactions. In effect, this a complex problem that needs the application for AI / machine learning to enable guided, efficient and effective optimization of the CHIPLET
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relevant PhD and medical degree alongside registration with the GMC at Specialist Registrar Garde or below. You will have a recent track record in histopathology and use of machine learning techniques, and