Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Nature Careers
- Oak Ridge National Laboratory
- ;
- Argonne
- Fraunhofer-Gesellschaft
- Nanyang Technological University
- California Institute of Technology
- ETH Zurich
- Forschungszentrum Jülich
- National University of Singapore
- Technical University of Denmark
- University of Cincinnati
- University of Dayton
- University of Washington
- AIT Austrian Institute of Technology
- Central China Normal University
- Cold Spring Harbor Laboratory
- European Magnetism Association EMA
- Free University of Berlin
- Johns Hopkins University
- King Abdullah University of Science and Technology
- Lawrence Berkeley National Laboratory
- Los Alamos National Laboratory
- Meta/Facebook
- NTNU - Norwegian University of Science and Technology
- Northeastern University
- Singapore Institute of Technology
- Stanford University
- Technical University of Munich
- Texas A&M University
- The Chinese University of Hong Kong
- The Ohio State University
- UNIVERSITY OF HELSINKI
- University of Arkansas
- University of California Davis
- University of California Davis Health System
- University of California Irvine
- University of Glasgow
- University of Maryland, Baltimore County
- University of Minnesota
- University of North Carolina at Chapel Hill
- University of North Carolina at Greensboro
- University of North Texas at Dallas
- University of Oxford
- University of Pittsburgh
- Washington University in St. Louis
- 36 more »
- « less
-
Field
-
Engineering, or a related field Strong experience in building and optimizing AI systems using PyTorch, TensorFlow, or JAX Practical knowledge of NVIDIA GPU programming (CUDA) and experience with inference
-
-performance computing, including parallel or GPU programming (MPI, OpenMP, CUDA, Kokkos, etc.) Familiarity with modern software development practices, including debugging, profiling, and version control
-
., DeepSpeed, FSDP, Ray, or MPI-based systems). Familiarity with GPU-accelerated computing (e.g., CUDA, NVIDIA ecosystem). Preferred Qualifications Education: No additional education beyond what is stated in
-
-based systems). Familiarity with GPU-accelerated computing (e.g., CUDA, NVIDIA ecosystem). Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications
-
one of the above fields Very good expertise in the programming languages Python and C/C++, the numba library, and in applying parallelization techniques using GPU programming (CUDA/OpenCL) and MPI
-
Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
-
implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
-
and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design
-
, CompTIA Network+/Security+; Experience with GPU environments (NVIDIA preferred), CUDA drivers, and related tools; Experience in research labs, AV systems, and hybrid on-prem/cloud setups. Job Duties and
-
CUDA. What we can offer you: An opportunity to play a key role in a multidisciplinary team driving innovation in the deployment of deep neural network (DNN) models across a continuum of edge-to-cloud