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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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in urban-scale building energy modeling, software development (esp. Python), or Artificial Intelligence/Machine Learning (AI/ML) Strong ideation, writing, and communication skills for establishing
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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred
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, enabling manual and automated adversarial testing at scale. Build and integrate AI-on-AI testing infrastructures, where AI models can actively challenge each other in adversarial contexts to detect
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platforms and autonomous systems, to characterizing global population risk with increasing spatiotemporal clarity, to designing GeoAI models for supercomputer-scale applications, geospatial science at ORNL is
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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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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-learning interatomic potentials (MLIPs) and phase field modeling, particularly related to materials for next-generation microelectronics (e.g. oxide ferroelectrics, 2D materials and related systems). Strong