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Field
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to iteratively refine models and optimize experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data
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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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. The candidate will contribute to projects focused on developing advanced machine learning models to quantify phenotypic and agronomic traits of crops, including corn, soybean, and other selected species
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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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, audio, pose, sensor, or behavioral data. Experience developing machine learning models for human behavior analysis. Interest in autism research, developmental science, digital health, or behavioral
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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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extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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of machine learning models. · Experience with statistical analysis, multivariate methods, or comparative and phylogenetic analyses. · Experience using high-performance computing (HPC), cloud computing
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machine learning, topological quantum materials, and/or related areas. The successful candidate will have access to HiPerGator, the fastest university-owned supercomputer in the United States (TOP500