74 scholarship-phd-agent-based-modelling Postdoctoral positions at Oak Ridge National Laboratory
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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Postdoctoral Research Associate to join the Microbial Engineering Group. In this role, you will develop next‑generation genetic tools for non‑model microorganisms, enabling precise genome engineering in
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separation, and biomass based chemical and fuel precursors. Selected candidate will be responsible for developing new and exciting ideas, building collaboration within and outside ORNL, and executing
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) Fixed-effects panel models Matching methods (PSM, CEM, nearest-neighbor) Regression-based normalization Longitudinal and cross-sectional data analysis. Experience working with large administrative
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candidate will manage several projects concurrently, help shape new research directions within their field, and collaborate closely with ORNL staff, industry professionals, and academic partners. Based within
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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modeling, and complementary characterization. We are particularly interested in candidates with backgrounds in optical spectroscopy, scanning probe microscopy, semiconductor materials, or condensed matter
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state-of-the-art high-performance computing. Key Research Areas: AI for Science: Research and development of large-scale AI models for science, focusing on pre-training, instruction-based fine-tuning, and
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credible claims of quantum advantage. Develop and apply physics-informed AI/ML and digital-twin capabilities to improve modeling, parameter inference, uncertainty assessment, and adaptive feedback between
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as