13 machine-learning-phd Postdoctoral positions at KINGS COLLEGE LONDON in United Kingdom
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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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engineering, or related disciplines who are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models
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skills. Aware of the ethical issues around working with Big Data. Desirable criteria Experience applying advanced statistical or machine learning methods to complex datasets. Evidence of involvement in
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statistical or machine learning methods to complex datasets. Evidence of involvement in grant writing or development of independent research ideas. A commitment to teaching the next generation of researchers
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. DVXplorer), and tactile/force sensors. Strong background in computer vision and deep learning, with practical implementation experience. Proficiency in programming with C++ and Python, including use of ROS
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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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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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are looking 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
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backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute
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and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU). Importantly, we adopt a flexible working environment within the lab and are happy