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Field
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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, written, and oral communication skills in English. Exhibit strong organisational skills and the ability to meet deadlines and complete projects. Have expertise in machine learning and/or programming (highly
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. We are now looking for: Three (3) Doctoral Researchers (PhD students) in Machine-Learning-Driven Atomistic Simulations The Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro
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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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are invited for the 2026 Research Fellowship awards. Up to four Research Fellowships will be awarded in this competition. Applicants should have submitted their PhD after 1 October 2024, or be on track
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists. Based across King’s Denmark Hill, Guy’s, St Thomas
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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models
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skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods