92 systems-science-"https:" "https:" "https:" "https:" "UCL" Postdoctoral research jobs at Argonne
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The Computational Science Division (CPS) at Argonne National Laboratory (near Chicago, USA) is seeking a postdoctoral researcher to enable exascale atomistic simulations of ferroelectric devices
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scale up (0.5 to 400 L) arrested methanogenesis systems to convert diverse low-value organic waste streams into value-added products (e.g., carboxylic acids), and support the development and scale-up
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, large-scale computational science, and simulation of networked physical systems Familiarity with techniques for sensitivity analysis and handling high-dimensional problems Experience in power grid
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engineering, controls and data systems, and internal and external scientific partners. Essential Duties and Responsibilities: Lead preparation of a review-ready engineering specifications document
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field Strong foundation in electrochemistry, electrochemical engineering, and chemical processing Demonstrated experience in mathematical modeling of electrochemical systems; knowledge of solid mechanics
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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of radiofrequency (MHz–GHz) nanoscale phenomena in systems relevant to microelectronics and quantum information science. Opportunities also exist for cross-platform studies integrating ultrafast TEM with ultrafast x
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The Nuclear Technologies and National Security (NTNS) Directorate is seeking a highly qualified and motivated Postdoctoral Researcher specializing in energy economics and supply chain analysis, with
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or equivalent. Knowledge and experience in electrochemistry, electrochemical engineering, or materials science and the ability to apply research principles from those fields to new systems. Skill in devising and
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance