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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 29 days ago
as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital role in our research enterprise and towards our
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, spatial and hydrologic modeling, statistical analysis, high-impact technical publishing, and collaborative proposal development. In support of NRRI’s mission to deliver applied science and sustainable
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laboratories. The broader goal is to enable AI-driven materials discovery, autonomous synthesis, and the development of high-quality, reusable datasets that support adaptive experimentation and long-term
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appropriate methodologies (20%) Modify system setup and process protocol to enhance robustness and efficiency (15%) Model process performance and conduct validation studies (15%) Document results and prepare
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modeling, computational modeling, quantitative biology, dynamical systems, stochastic processes, complex systems, or related quantitative approaches. Candidates should have experience developing
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applications. Research activities may involve geophysical forward modeling, AI-driven geophysical inversion, seismic monitoring and imaging, scientific machine learning, depending on the specific research focus
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, Environmental and Planetary Science Division. The aim of the position is to advance the frontiers of structural biology by developing AI-driven methods for improving crystal quality at scale. This role will
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of hydrometallurgical processes such as leaching, solvent (liquid-liquid) extraction, and adsorption; evaluating process performance and operability; developing test plans and standard operating procedures; assisting
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%) • Modify system setup and process protocol to enhance robustness and efficiency (15%) • Model process performance and conduct validation studies (15%) • Document results and prepare reports and papers
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across heterogeneous data types such as clinical, imaging, omics, text, and experimental data. The work will include developing approaches for continual model improvement, adaptive federated training