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Field
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maintaining the DataSpaces (https://dataspaces.sci.utah.edu/download/ ) project – a high-performance distributed data management framework designed for in-memory data staging and coordination in scientific
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Postdoctoral Researcher position. Part-time 20 hours/month distributed unequally, gross hourly salary of 240 lei, fixed period until June 30, 2026. The project is entitled “A Study of Consumer Trust in Online
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Postdoctoral Researcher position. Part-time 32 hours/month distributed unequally, gross hourly salary of 80 lei, fixed until June 30, 2026. The project is entitled “A Study of Consumer Trust in Online Reviews
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Postdoctoral Researcher position. Part-time 80 hours/month distributed unequally, gross hourly salary of 80 lei, fixed period until June 30, 2026. The project is entitled “A Study of Consumer Trust in Online
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for the concept of optimal transport for inverse problems. Optimal transport is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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Interpersonal and communication skills with demonstrated ability to work within a geographically distributed networks of collaboration Proven experience in developing and implementing machine learning models and
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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energy goals. ESIA also develops, deploy, and advance grid technologies that ensure a robust and secure U.S. grid transmission and distribution system. We collaborate with government agencies as
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and Mission Science team located at ESTEC (Noordwijk, Netherlands) to align the development of AI-driven methodologies and algorithms with the CHIME mission. Technical competencies Knowledge relevant