Sort by
Refine Your Search
-
and contributing to reusable research software when appropriate. Position Requirements Required skills, experience and qualifications: PhD in computer science, applied mathematics, electrical
-
research. The position plays a central role in strengthening the CNM user science program, with a particular focus on electron microscopy and synchrotron-based X-ray microscopy at the Advanced Photon Source
-
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
-
) 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
-
may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
-
capable of hypothesis generation, experimental design, data analysis, and iterative learning in biological contexts. This project seeks to transform how computational and experimental biology research is
-
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
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
-
have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI
-
The position is part of a new collaboration between Argonne National Laboratory, the University of Notre Dame, and UIUC, supported by the Quantum Information Science Enabled Discovery 2.0 (QuantISED