Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Northeastern University
- SUNY University at Buffalo
- The University of Chicago
- University of Copenhagen
- University of Texas at Arlington
- Lawrence Livermore National Laboratory
- New England HERC
- Stanford University
- University of Central Florida
- University of Delaware
- University of North Carolina at Chapel Hill
- Yale University
- CRANFIELD UNIVERSITY
- Chalmers University of Technology
- City of Hope
- Colorado State University
- Duke University
- Florida Atlantic University
- Georgia Southern University
- Harvard University
- Indiana University
- Marist College
- Michigan State University
- Oak Ridge National Laboratory
- Rutgers University
- University of Arkansas
- University of California
- University of California, Los Angeles
- University of Illinois at Urbana Champaign
- University of North Carolina at Charlotte
- University of Oklahoma
- University of Pennsylvania
- University of Utah
- 23 more »
- « less
-
Field
-
-time computing on heterogeneous processor types Optimized GPU computing and exploitation of GPU architectures for HPEC (tensors, multi-GPU instantiations, advances in GPU for AI/ML) Compute-focused
-
, Elastic Stack, or similar monitoring platforms. Experience supporting GPU computing environments, including NVIDIA drivers, CUDA , GPU scheduling, monitoring, and performance optimization; familiarity
-
are part of a global computing grid used to reconstruct and analyze particle collisions recorded by the ATLAS detector at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland (https
-
analysis. Perform analyses in high-performance computing (HPC) and GPU-enabled environments. Collaborate with clinicians, computational scientists, and data scientists in a highly interdisciplinary setting
-
( https://theory.rutgers.edu/ ). The successful candidate will develop new GPU -accelerated software for alchemical free energy and new AI-enhanced quantum mechanical force fields built using
-
single-mode and multimode semiconductor laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where
-
and the tools required to make data driven discoveries that increase our scientific knowledge and improve the quality of life. We architect ecosystems of large-scale commons of research data, computing
-
quickly and functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed
-
roles on multidisciplinary teams supporting cutting-edge research and engineering projects, deploying workflows on large-scale computing environments, leveraging ORNL’s Frontier for its dense GPU-based
-
, engineering and the life sciences. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit