87 computer-engineer-"https:"-"https:"-"https:"-"LGEF" Postdoctoral positions at Argonne
Sort by
Refine Your Search
-
for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation
-
Recent or soon-to-be completed (typically within the last 0-5 years ) Ph.D. in Computer Science, Electrical Engineering, or a related field. Demonstrated research expertise in AI and machine learning, with
-
Qualifications: Ph.D. (completed within the past 0-5 years) in computer science, electrical engineering, applied mathematics, or a related field. Strong proficiency in Python, with additional experience in C, C
-
computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
-
in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
-
The Nanoscience and Technology Division (NST) at Argonne National Laboratory invites applications for a postdoctoral researcher to lead cutting-edge efforts in electrically driven ultrafast electron
-
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
-
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
-
recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Computational Materials Science, Chemical Engineering or a closely related field. 2. Technical Expertise
-
) 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