68 condition-monitoring-machine-learning Postdoctoral positions at University of Oxford
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. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old Road Campus. You will join an interdisciplinary team of researchers spanning imaging science, machine learning
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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experiments for investigating the neural mechanisms underlying habitual behaviours and learning adaptation to uncertainty. You will use fMRI and neurostimulatory techniques (ultrasound neurostimulation and/or
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine
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, learning under uncertainty) that is of an international standard, and that is carried out expertly, rigorously and in accordance with ethical guidelines. You will also participate actively in the lab
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proof-of-principle repetition-rate and staging experimentation. The successful candidate will perform duties that include developing/using particle-in-cell computer codes hosted on local and national high
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, ensuring they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (e.g. Statistics, Machine Learning
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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-performance or cloud computing environments. Need strong data management and database skills, expertise in clinical phenotyping ontologies and the application of machine-learning/AI methods to biomedical data