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, that can be realised using cloud compute infrastructure and other novel deployment architectures. Given our team's existing research skillset in novel machine learning approaches, we are recruiting a
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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple
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algorithms and neural network models, drawing on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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Are you a computer scientist or machine-learning researcher interested in making medical-imaging AI work reliably beyond the dataset on which it was developed? We have an exciting two-year Grade 7
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advanced statistical modelling, econometrics or machine learning with a particular focus on analysing large-scale behavioural data? Are you interested in developing new methodological approaches capable
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learning algorithms using Python/R. Learns to fit machine learning models on biological data. Identifies the specific datasets and machine learning algorithms. Implements the algorithms in Python/R and fit
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data. This position provides excellent opportunities to gain research experience in machine learning, medical image analysis, multimodal foundation models, and clinical AI while working in a highly
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learning models. Strong background in machine learning optimization, with experience in distributed or multi-node collaborative training; experience in large-scale training is highly desirable. Strong
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address