54 formal-method-phd Postdoctoral positions at Oak Ridge National Laboratory in United States
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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
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methods to work with a team of scientists in CSD to model chemical reactions important to determine the longevity of amorphous materials. That mechanistic information will be incorporated into process-based
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Requisition Id 15421 Overview: The Multiscale Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related
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: https://www.ornl.gov/content/research-integrity Basic Qualifications: To be eligible you must have completed a PhD in chemistry, physics, engineering, or a related field with in the last 5 years
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Requisition Id 15510 Overview: Do you have a passion for applying artificial intelligence (AI) methods for accelerating scientific discoveries and an ability to think outside of the box in a
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Requisition Id 15716 Overview: We are seeking a Postdoctoral Research Associate - Meteorologist who will focus on the development, testing, and deployment of data analysis methods for a variety of
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
and 2D memristive materials). As a Postdoctoral Research Associate, you will contribute to research in these areas, bridging state-of-the-art atomistic and mesoscopic simulation methods as indicated
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in evolutionary biology, plant biology, genomics, bioinformatics, mathematics, statistics, computer
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data