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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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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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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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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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team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS
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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
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on physics analysis and artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard
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the development of high-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning