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Field
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) for data analysis, numerical modeling, or machine vision Supervision Exercised This position carries a high degree of independence. The Postdoctoral Associate will meet with the PI at least twice per week
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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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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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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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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: 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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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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researchers working on hyperspectral imaging, radiative transfer modelling, machine learning, agronomy, and plant genetics. You will also work with HYDRA-EO partners in Netherlands, Spain and Italy
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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