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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field
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this work we investigate how molecular materials coupled to solid-phase scaffolds may influence molecular motion, photoinduced kinetics, charge dynamics, and assembly durability. The work will target
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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experiments. Develop reinforcement learning models to improve gate fidelity. Leverage CNM’s state-of-the-art facilities, including the nanofabrication cleanroom and the Quantum Matter and Device Lab’s dilution
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and datasets • Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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technical challenges, develop innovative solutions, and implement practical strategies. Skilled communication and interpersonal skills at all levels of the organization. Ability to model Argonne’s core values
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technical challenges, develop innovative solutions, and implement practical strategies. Skilled communication and interpersonal skills at all levels of the organization. Ability to model Argonne’s core values
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mechanistic studies of catalytic systems Strong written and oral communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Familiarity with ligand design
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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior