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with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods
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addition to conventional tools such as XRD, BET, XRF, SEM, TEM, etc. Collaborate with computational modeling and artificial intelligence/machine learning teams to improve battery performance, using rational design
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models for turbulent reacting flows. Development and application of machine learning tools in one or more of these areas: chemical kinetics, turbulent combustion, and extreme event prediction. Job Family
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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in soil processes, plant physiology, plant traits, and species composition Strong data analysis skills, including proficiency in R or Python coding, and/or machine learning techniques Excellent writing
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encompass: Catalysts Synthesis: Utilize your expertise in materials synthesis to develop novel catalysts guided by machine learning algorithms Catalyst Performance Evaluation: Utilize aqueous electrochemical
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their environmental impacts including energy use, water use, emissions, and resource depletion. Coursework and some project experience in application of mathematical optimization, statistics, or machine
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intelligence Experience with multimodal knowledge graphs Experience with developing machine-learned interatomic potentials and deploying them in large-scale molecular simulations. Experience with scientific
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management through status updates, technical research reports, project presentations, and other regular channels. Develop technical ideas and proposals to advance the understanding of molten salt
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), widely utilized for characterizing biomacromolecules, polymers and other nano-scale structures. The successful candidate will join an interdisciplinary team and leverage machine learning or artificial