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experience: Familiarity with machine learning hardware Familiar with high performance scientific infrastructure Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term
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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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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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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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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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, inclusive, and accessible environment where all can thrive. Additional Preferred Qualifications: Working knowledge of power system protection and control. Familiarity with Machine Learning. Familiarity with
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of Pittsburgh, University of Texas Medical Branch and BARDA, aimed at advancing pandemic bio-preparedness through AI-driven forecasting. With advances in machine learning frameworks and emerging accelerator
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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied