Sort by
Refine Your Search
-
well as in industry and at other national laboratories. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years)in Engineering or similar program. At least 2
-
on GitHub: https://github.com/AD-SDL/MADSci AD-SDL organization on GitHub: https://github.com/AD-SDL Job Family Research Development (RD) Job Profile Computational Science 3 Worker Type Regular Time Type Full
-
is available for the design, licensing and operation of MSRs and other energy systems. Develop and perform laboratory tests and electrochemical operations with molten salts that may contain actinides
-
The Chemical Sciences and Engineering Division at Argonne National Laboratory invites applications for a regular, full-time Assistant Computational Chemist / Chemical Engineer position
-
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
-
platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will
-
the Argonne energy storage ecosystem, working closely with the Argonne Collaborative Center for Energy Storage Science (ACCESS). Continue a world-class research program related to energy storage and
-
Computational Science and AI Group (CAI) at APS. The successful candidate will have access to Argonne’s exceptional ecosystem of facilities and expertise, including the upgraded APS, CNM’s advanced synthesis and
-
The Chemical Sciences & Engineering Division is seeking qualified candidates for Group Leader of the Catalysis research group. As a group leader, the role provides scientific leadership and
-
together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning