Sort by
Refine Your Search
-
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
-
, morphology, and device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and
-
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
-
. 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
-
and tools to support engineering analyses of power grid planning and operations. Some of these tools will require the development of underlying optimization methodologies and analytics frameworks
-
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
-
research in radiochemical separations and advanced aqueous recycling of used nuclear fuel (UNF). The successful candidate will contribute to the development and optimization of advanced aqueous recycling
-
codes and making use of high-performance computing (HPC) tools. Develop accurate, computationally efficient physics-based and data-driven CFD models which the candidate will use to simulate the chain of
-
UNIX environment, scientific computing languages such as python or C. Demonstrated background in computational modeling or numerical simulation for science. Ability to work within a multi-lab