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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
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further including to automated platforms to generate large statistical data sets. We will also experiment with untried higher spatial resolution techniques. The large, multi-dimensional data sets will be
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., knowledge representation and reasoning) and bottom-up (e.g., machine learning) methods to study the representation of geographic categories and processes. While we welcome applicants from a broad range of
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communities bordering the West Nile, Lake Albert, and Lake Victoria. To be considered for the role, you should hold (or be close to completion of) a PhD/DPhil in Health Data Science, along with relevant
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, working with some of the world’s largest single-cell data assets, and in the context of the world’s largest longitudinal population studies, many hosted here at the Big Data Institute, as well as other
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for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs