74 scholarship-phd-agent-based-modelling Postdoctoral positions at Oak Ridge National Laboratory
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Qualifications: A PhD in Computational Chemistry, Physics, Chemical Engineering, Materials Science and Engineering, or a related field completed within the last 5 years Experience in theoretical and computational
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disturbance at scale. A cohort of postdoctoral researchers will be hired across multiple institutions to collaboratively support scientific advances guided by AI/ML, remote sensing, process-based modeling
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temperature. You will use and help refine techniques developed at ORNL that estimate shear strength based on the brittle-to-ductile transition, hot hardness testing, spherical indentation, and other new pending
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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simulations. Design, develop, and validate physics-informed AI/ML models with features from electronic structure, spectroscopy to control materials growth and emerging functionalities. Develop and train agentic
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of irradiated ceramics and alloys for tritium technology development using advanced experimental and computational methods. The researcher will perform characterization of model systems using techniques such as
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data analytics, and manufacturing process optimization. Develop and apply models, algorithms, or data analysis workflows to support machining process understanding, machine tool characterization, process
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level for comparison with carbon/water exchange based on eddy flux measurements Use AI/ML data integration, modeling and trait databases to scale up ecophysiological mechanisms of ecosystem water and
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-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work
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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