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Field
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into closed-loop control frameworks Test and validate control algorithms through simulation, hardware-in-the-loop testing, and/or physical testbeds Apply machine learning techniques to predict thermal system
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materials science applications and algorithms for OLCF supercomputers Develop and apply advanced, compute-intensive materials simulation methodologies to study the fundamental properties of molten salt
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Responsibilities The incoming postdoctoral researcher will lead or contribute to key research activities within the LU-PCG, advancing control theory, machine learning algorithms, and integrated simulation workflows
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(AI) and machine learning (ML) methodologies. The position involves annotating clinical data, collaborating with AI/ML/NLP teams, developing algorithms, and generating insights to improve patient care
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of conducting rigorous research and producing high-quality outputs. Ability to network with industry partners. Expertise in machine learning algorithms, artificial intelligence, game theory, optimization
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(AI) and machine learning (ML) methodologies. The position involves annotating clinical data, collaborating with AI/ML/NLP teams, developing algorithms, and generating insights to improve patient care
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to the development of remote sensing algorithms, simulation and modeling capabilities, and artificial intelligence/machine learning (AI/ML) methods in support of the goals of the joint NASA/USGS Landsat mission
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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in social media, algorithm design, and interventions to support well-being 40% Leading the system development and implementation of the mobile app 20% Dissemination via articles, conference
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quantitative genetics and maize breeding. -Utilizes skills and knowledge in these and other areas to complete research projects leveraging new data extraction and analysis algorithms and connecting