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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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for energy yield prediction. - Develop models for performance loss rate analysis. - Conduct time series analysis and apply machine learning techniques to assess PV and energy storage system performance
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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workers, staff, and faculty to teach and support these workshops. 3. Assist with the development and support of funded DH scholarship, both at USM and at partner institutions across the state, including
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spillovers. The postdoc will contribute to a collegial and collaborative lab dynamic. Qualifications Essential Qualifications: ● A PhD in ecology, epidemiology, or related discipline ● Established publication
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, skills and abilities: -Proficiency in image analysis using machine learning methods. Continued employment in this position is contingent upon availability of funds. Please attach the following to completed
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experience: Familiarity with machine learning hardware Familiar with high performance scientific infrastructure Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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data set to be used for Machine Learning (ML). They will validate a predictive model generated by ML by preparing new samples with ideal monomers and contrasting their properties with predicted values