Sort by
Refine Your Search
-
The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
-
chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU
-
The Data Science and Learning Division (DSL) of the Computing, Environment and Life Sciences Directorate (CELS) and the Materials Science Division (MSD) of the Physical Sciences and Engineering
-
will be working with ALCF’s technical teams (e.g., AI/ML, Data Science, Performance Engineering) and will focus on collaborative APEX research projects. We are looking to hire four Postdoctoral
-
infrastructure, autonomous systems, and materials science. You will develop and apply advanced data-driven methodologies to accelerate discovery in materials design, characterization, and synthesis. The role
-
methods for designing safer and more reliable components. The researcher will also contribute to technical reports, conference papers, and journal publications, and present findings at technical conferences
-
The Q-NEXT National Quantum Information Science and Research Center based at Argonne National Laboratory invites applications for a postdoctoral position to conduct research in the field of material
-
. The cosmology effort at Argonne includes staff members from the CPAC group, the Computational Science division, and the HEP Detector Group. The group also includes many postdocs, and a number of graduate and
-
computational science expertise. The ALCF has an opening for a postdoctoral position in data management targeting AI applications at scale. The successful candidate will join the AL/ML group, a vibrant
-
collaboration with team members. Skilled written and verbal communicator, including the ability to present complex information so that it is understandable to a broad audience. Computer skills relevant for data