55 software-formal-method-phd Postdoctoral positions at Oak Ridge National Laboratory
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experiments in the SNS ring including experiment design, and data analysis. Develop software for data acquisition and analysis as needed. Perform simulations using a well-tested model of the SNS ring to
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interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Basic Qualifications: A PhD in in condensed matter physics, theoretical physics, quantum information, or a closely related
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating
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, and measure success. Basic Qualifications: A PhD in materials science and engineering, mechanical engineering, aerospace engineering, polymer science, or a related discipline completed within the last
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research methods on large, domain-specific scientific datasets. Major Duties/Responsibilities: Designing and developing foundational AI-driven techniques for the generation and exploration of complex, large
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domain experts—such as those in neutron scattering and urban science—to apply and evaluate research methods on large, domain-specific scientific datasets. Major Duties/Responsibilities: Designing and
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projects relevant to catalysis and critical materials. Contribute to methods development and integrate data science to accelerate simulations, analyze large datasets, and extract properties. Work in multi
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methods towards improving our understanding of unique target materials. You will be working with scientists, engineers, technicians, and safety and quality assurance staff to support material testing and
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to the implementation and perpetuation of values and ethics. Basic Qualifications: A PhD in inorganic, organic, polymeric, or physical chemistry or a closely related field, completed within the last five years. Preferred
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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