27 structural-reliability-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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on physical robotic systems. Establish quantitative measures for localization drift, mapping accuracy, and navigation reliability. Publish results in peer-reviewed journals and conference proceedings, and
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preparation of well-characterized target compounds. Apply a range of analytical and characterization techniques to evaluate chelator structure, affinity, selectivity, and metal complexation, including radio-TLC
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adaptive search strategies. Contribute to research in uncertainty quantification, surrogate modeling, and other methods that improve the robustness and reliability of AI-driven scientific applications
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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this role, you will study buried interfaces, defects, and charge-transfer processes in vertically integrated semiconductor structures. The research will focus on low-dimensional and emerging materials
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. Knowledge of fiber manufacturing processes and structure-property relationships is a plus. Strong analytical and problem-solving skills. Excellent written and verbal communication abilities. Experience in
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Requisition Id 16540 Overview: We are seeking a Postdoctoral Research Associate who will develop and apply computational methods based on electronic structure theory and artificial intelligence
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, implementation and application of ab-initio electronic structure and their application to solid state systems. Proficient skills in common scientific programming languages such as C/C++ and Fortran Experience in
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and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in
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, a proven publication record, and effective interpersonal skills. Preferred Qualifications: Knowledge of graph neural networks and other geometric deep learning approaches for graph-structured