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, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object
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do things, especially considering recent advancements in AI technology. The position will include developing radiomics and deep learning models from contrast-enhanced computed tomography images
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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latitudes and deep convection in marine and continental environments). You will work closely with colleagues at Leeds and Warwick (who are developing and validating the toy/atomistic models) to translate
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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. in Linguistics, Computer Science, Cognitive Science, or a related field by the start date Strong background in computational linguistics or deep learning Demonstrated interest in at least one of
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international work environment Learn more about CQT at https://www.cqt.sg/ Job Description The successful candidate will drive research at the intersection of Condensed Matter Theory, Quantum Computing and
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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time-management skills are a must. Research grant writing experience and a publication track record are highly desired. Preferred Qualifications: Expertise in machine learning, deep learning, natural