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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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environmental trade-offs. Contribute to projects involving capacity expansion, production cost modeling, and equilibrium modeling of power systems. Design and apply mathematical optimization models, including
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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knowledge of contemporary high-energy physics and mathematical methods relevant to physics applications Effective verbal and written communication skills Ability to model Argonne’s core values of impact
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, Earth system science, applied mathematics, or a related field. Experience with one or more coastal/ocean modeling systems (e.g., MPAS-Ocean, ADCIRC, ROMS, NEMO, WAVEWATCH III) and familiarity with
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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Mathematics, or a closely related field. Design and optimize multimodal LLMs to encode, fuse, and reason over heterogeneous scientific data from diverse modalities such as numerical tables, text, and images
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venues Position Requirements Required skills and qualifications: A PhD degree completed within the last 0-5 years (or soon to be completed) in numerical analysis, applied mathematics, computational science
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We invite applications for a postdoctoral position in the Functional Coatings Group in the Applied Materials Division at Argonne National Laboratory to conduct advanced research in energy storage. A primary goal of this work is aimed at advancing next-generation, lithium-ion technology through a...
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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety