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state-of-the-art high-performance computing. Key Research Areas: AI for Science: Research and development of large-scale AI models for science, focusing on pre-training, instruction-based fine-tuning, and
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: Conduct research and create innovative ideas and solutions for urban planning, transportation, and grid integration issues, focusing on sustainability and resilience. Develop innovative equity-based
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Independently and collaboratively lead field
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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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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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and