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applications, (2) design and architecture of integrated, hybrid, atomistic simulation software packages (e.g., LAMMPS) and DL models, and (3) documentation, verification and validation, and software quality
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for simulating atomic nuclei, as well as preparing data and using machine learning models for investigating how the properties of atomic nuclei connect to fundamental questions in physics, such as constraining
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) for lattice model simulations. Experience working in a multi-disciplinary research environment. Demonstrated written and oral communication skills, a proven publication record, and effective interpersonal
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analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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., code interpreters, simulation frameworks, databases, lab instruments) and evaluation for long-horizon tasks. Experience with RL and post-training (reward modeling, preference learning, offline/online RL
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, remote sensing products, and model simulations. Use these datasets to inform model development, calibration, and validation efforts. Collaborative Research: Work closely with a multidisciplinary team of
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include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work in applications
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photosynthesis to join the new pilot study of Generative Pretrained Transformer for genomic photosynthesis (GPTgp). The GPTgp project aims to develop a foundational holistic model of photosynthesis that will scale
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and