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for building envelopes of new construction and retrofits to enable DOE’s energy efficiency goals. Our synergistic research areas include building science, material and system development, and advanced
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structure, and simulation of coupled difference or differential equations. Experience calibrating simulation models against sparse, indirect, aggregated, or otherwise limited observations. Familiarity with
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alloys 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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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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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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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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. 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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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
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pipelines for structured and unstructured data Experience with multimodal datasets (e.g., imaging, time-series, and process data) Experience with API-based data services, workflow automation, or integration