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anthropogenic factors using deep learning and vision transformer models, (2) Incorporating past factor trends for more realistic predictions under the non-equilibrium hypothesis, (3) Leveraging transfer learning
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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science tools, such as machine learning methods, causal inference, particularly in economic and social science applications. Prior experience working with Large Language Models is a plus. Experience working
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experience. Applications from all backgrounds are welcomed. Learn more about Boise State and living in Idaho’s Treasure Valley at https://www.boisestate.edu/about/
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benefits package, including health and life insurance, generous paid leave and retirement programs. To learn more about UofSC benefits, access the "Working at USC" section on the Applicant Portal at https
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)/machine learning (ML) applications in the power grid. * Experience with power system modeling, simulation, dynamics and/or optimization, phasor and electromagnetic transient (EMT)-based modeling, and Python
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). This position focuses on the machine learning methodology of the project, aiming to: Develop probabilistic spatio-temporal models that integrate uncertainty from climate projections into land-use forecasts
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and trustworthy machine learning-based clinical prediction models. Funded by the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR), the project aims
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possible for up to 1 day/week. You will join an interdisciplinary team of researchers spanning imaging science, machine learning, genetics, and population health, working closely with collaborators
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/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data