Sort by
Refine Your Search
-
. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
-
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
-
environment that brings together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for
-
accelerator physics, beam dynamics, or free-electron-laser science. Experience with machine learning, surrogate modeling, optimization, or AI-enabled scientific computing. Experience working with complex
-
complex algorithms. Experience using computing clusters for simulation and data analysis. Experience with accelerator modeling and simulation codes, such as Elegant and Cheetah. Experience with SDDS, Tcl/Tk
-
closing this gap. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary
-
winds, land–atmosphere interactions, or aerosol–cloud interactions Strong experience in numerical modeling and high-performance computing • Experience applying AI/ML methods to model development, with
-
with computational scientists in other divisions, including Mathematics and Computer Science (MCS) and the Argonne Leadership Computing Facility (ALCF). The group has significant involvement in three
-
computing environments. - Strong written and oral communication skills, with the ability to work independently and collaboratively. - Ability to model Argonne’s core values of impact, safety, respect
-
nitrogen-vacancy (NV) diamond quantum magnetometry for high-energy physics experiments. The HEP Division performs cutting-edge research leveraging advanced detector development, high-performance computing