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be expected to model prototypes of the fiber optic based sensors using standard Multiphysics software, perform out-of pile laboratory testing of the instruments in representative environments, and
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field experimental ecophysiology measurements Experience applying ML/AI to biological or environmental data (e.g., transformer, state space models, multi-layer perceptrons, convolutional neural networks
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such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression
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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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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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models of gas transport and process behavior in industrial systems Collaborate with a team of scientists from across the national laboratory complex on modeling efforts Extend process flow modeling across
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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of next-generation semiconductor devices. The candidate will collaborate with researchers across CNMS, ORNL, and the broader NSRC network working in materials synthesis, device fabrication, theoretical
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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) project, an interdisciplinary and multi-institutional collaboration focused on disturbance-driven ecosystem transitions and their impacts across the U.S. Gulf Coast. EGRET employs an integrated model