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and adapt assimilation schemes based on generative deep learning methods (such as flow matching and diffusion models). The candidate should have previous experience in data assimilation and/or deep
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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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Experience in modelling of multiphase systems (e.g. liquid/solid, gas/liquid, liquid/liquid, or gas/solid) Experience in application of machine learning approaches. Experience in scientific computing
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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. Desirable experience in AI-assisted image analysis, machine learning, computational modelling, or building predictive models from biological imaging or cell–material interaction datasets. Strong experience in
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programs. To learn more about UofSC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant or Time-limited positions may be eligible for all, some, or
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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop