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learning with knowledge-based inference, validated by independent experiments and partially supervised by human-in-the-loop systems. A key question will be how agentic AI and foundation models can be
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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mathematics and learn more about modeling of atmospheric or oceanic flows, or the motion of charged fluids such as plasmas? We are looking for a Doctoral student to become part of Klas Modin's group
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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advising models and proactive, inclusive pedagogy. Experience in fostering collaboration and providing guidance to academic advising teams. Skills and Knowledge: Deep understanding of academic advising
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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automation, and deployment frameworks. Familiarity with machine-learning integration, including packaging models for production, reproducible workflows, and environment management. Strong programming skills in
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working on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and