60 Computer-Science-"https:"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions at Argonne
Sort by
Refine Your Search
-
successful candidate should have completed a doctorate degree in physics, materials science, or related field. They should be experienced in nanofabrication with demonstrated interest in quantum
-
The Materials Science Division is seeking a highly motivated Postdoctoral Research Associate to join our Quantum States and Crystalline Matter Group. This exciting research opportunity focuses on the
-
Argonne National Laboratory’s Materials Science Division (MSD) invites applications for a Postdoctoral Appointee in computational materials chemistry and atomistic modeling to investigate the
-
postdoctoral researcher will join a highly collaborative team of physicists and engineers across Argonne’s High Energy Physics (HEP) and Materials Science Division (MSD), while also working closely with
-
Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of chemistry, chemical engineering, materials science , or equivalent Required hands-on experience with co-precipitation
-
PhD (within the last 0-5 years) in field of Chemical Engineering, Mechanical Engineering, Materials Science, Chemistry, or a related field A deep understanding of electrochemistry, electrochemical
-
Nanoscale Materials (CNM). CNM is a U.S. Department of Energy Office of Science user facility that offers world-class expertise and state-of-the-art instrumentation to enable leading-edge research in
-
The Surface Scattering and Microdiffraction (SSM) group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory is seeking Two Postdoctoral Appointees
-
exciting opportunity to work at the intersection of interfacial science, geochemistry, and advanced characterization in a highly collaborative research environment. The successful candidate will be
-
and Membrane Manufacturing group perform science-based membrane synthesis and scaleup development by using roll-to-roll manufacturing and machine learning enabled in-line characterization and quality