30 collaborative-learning Postdoctoral research jobs at Oak Ridge National Laboratory
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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, characterize physiological impacts, and link genotypes to phenotypes using next‑generation sequencing, systems biology, and complementary analytical approaches. This position requires close collaboration with
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integrating human-in-the-loop reinforcement learning approaches. Responsible AI: Exploration of privacy-preserving AI techniques, enhancing AI safety, and addressing vulnerabilities and defenses in Large
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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Dr. Jeffrey Warren and Dr. Lianhong Gu and collaborate with researchers in the ORNL Terrestrial Ecosystem Science (TES) Scientific Focus Area (SFA) to integrate experimental measurements and trait
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Laboratory (ORNL). The selected candidate will work in a highly collaborative environment, leveraging state-of-the-art neutron and X-ray characterization facilities to develop a mechanistic understanding of
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opportunities to develop research methodologies as well as collaborate with experimental groups at Oak Ridge National Laboratory (ORNL). This position resides in the Materials Theory Group in the Materials
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publications, and conference papers; present research findings to sponsors and diverse audiences. Contribute to idea generation and proposal development. Collaborate effectively within multidisciplinary teams
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge