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multidisciplinary team of scientists and High Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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positions is to work on AI/ML with applications to cosmological modeling and surveys. Another open position is to work with Matthew R. Becker on weak gravitational lensing analysis with Rubin LSST data
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listed will be considered). Knowledge in the following areas: Li-ion batteries – including fabrication of coin cells and performing electrochemical performance testing, characterization of carbonaceous
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material property database for composites. The candidate will utilize the database to develop AI models for composite discovery. The candidate will work with a multidisciplinary team to set up finite element
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The Cosmological Physics and Advanced Computing (CPAC) group at Argonne National Laboratory invites applications for a postdoctoral researcher to work closely with Dr. Lindsey Bleem
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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
part of the DOE–BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC) , a multi-facility collaboration integrating experimental modalities and simulations to enable an orchestrating
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. The postdoctoral researcher will work closely with scientists at the Materials Science Division and the Advanced Photon Source (APS) as well as collaborate with external partners to exploit state-of-the-art
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candidate will lead efforts in materials synthesis, in situ/operando characterization, and catalytic performance evaluation. This role offers a unique opportunity to leverage CNM’s advanced characterization