55 phd-in-computational-mechanics-"Prof"-"Prof" Postdoctoral positions at Argonne in United States
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methods that aim to transform scientific discovery and leverage high-performance computing. Specifically, this will include research in : 1. Developing large-scale agent-based and other complex systems
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral researcher position in the field of hybrid quantum computing. This exciting project
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in computational research Comprehensive understanding of quantum mechanics and electronic structure theory is critical Experience with CFD (e.g., the use of OpenFoam or ALDFoam) and microkinetic
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We are seeking a highly motivated Postdoctoral Researcher with expertise in computational biology, deep mutational scanning data, and generative artificial intelligence (AI). The successful
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking postdoctoral researchers to work on distributed quantum computing. The project aims to develop superconducting
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detectors while also having flexibility to pursue your own research interests. Research Focus Participate in a detector R&D program aimed at developing superconducting nanowire sensors to enable
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We are seeking a Postdoctoral Appointee to work in the Mathematics and Computer Science (MCS) Division of the Computing, Environment, and Life Sciences directorate (CELS) of Argonne National
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(LOHCs) Perform detailed studies to understand reaction mechanisms, determine reaction kinetics, and measure the heat of adsorption of LOHCs in porous materials Collaborate with multidisciplinary teams
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mechanisms through reaction kinetics analysis and other physical organic and inorganic techniques Perform detailed in situ / operando studies of catalysts using X-ray Absorption and Emission Spectroscopies
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use modeling tools such as computational fluid