48 computing-"https:"-"https:"-"https:"-"Kiel-University" Postdoctoral positions at Argonne
Sort by
Refine Your Search
-
multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
-
physics (HEP) and nuclear physics (NP) experiments. The successful candidate will be a key member of a multidisciplinary co-design team integrating materials science, computing, and device engineering to
-
analysis Interdisciplinary Collaboration - Experience working in cross functional teams including molecular biologists, chemists, radiation experts and computational biologists Core Values - Ability to model
-
) in the field of accelerator physics or a closely related science and engineering discipline Strong experience developing and applying computational modeling and simulation Familiarity with accelerator
-
information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
-
. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1
-
Extensive knowledge of Microsoft Excel and good computer programming skills Knowledge of techno-economic analysis and life cycle analysis Experience working with Argonne’s EverBatt model, GREET model, and
-
limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory. The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division
-
and program managers. Position Requirements Minimum Education / Experience Requirements: A Ph.D. in physics, applied physics, electrical engineering, or related field. Additional Requirements: Normal
-
experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy