Sort by
Refine Your Search
-
in experimental physics and superconducting device development, with a focus on advancing multipixel single-photon camera technology and multiplexed readout for quantum information science applications
-
research will involve synergetic collaborations with a multi-disciplinary team involving engine modelers, CFD experts, and computational scientists to enhance the predictive capability for next-generation
-
projects in artificial intelligence, materials engineering, chemistry, and beyond at Argonne National Laboratory. Position Requirements Recently completed PhD within the last 0-5 years in computer
-
instrument proposed under a DOE Major Item of Equipment (MIE) effort. Building on two decades of APS XRS capability (including the LERIX program at 20-ID) and recent commissioning work at Sector 25
-
The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
-
field. Solid knowledge, and independent research capability in optimization, computing, power system engineering with track records of publications. Proficient in implementing control and optimization
-
at conferences and ALCF/DOE venues. Position Requirements Required Skills and Qualifications: Ph.D. in Computer Science, Physics, Chemistry, Biology, Engineering, Mathematics, or a related computational discipline
-
. The successful candidate will be a key contributor to a multidisciplinary co-design team spanning material science, computing, and electronic engineering, with the goal of enabling next-generation detector
-
science, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
-
Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow