Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Oak Ridge National Laboratory
- University of Central Florida
- Aalto University
- Argonne
- CEA
- Michigan State University
- National University of Singapore
- Northeastern University
- SUNY University at Buffalo
- Technical University of Denmark
- Technical University of Denmark (DTU)
- University of Lund
- University of North Carolina at Chapel Hill
- University of Utah
- 4 more »
- « less
-
Field
-
Engineering at Scale : Profile, model, and optimize distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross
-
: Experience with CUDA programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high
-
Shell scripting. • HPC & Containerization: Solid understanding of HPC environments, MPI, CUDA, Slurm workload management, and containerization tools like Apptainer. • Cloud Infrastructure: Proven
-
, AlphaFold2/3). Familiarity with Docker/Singularity for reproducible HPC environments. Experience with CUDA -level optimization or debugging hardware-specific performance differences. Basic knowledge
-
CUDA Experience with graphical frameworks such as Qt (PySide6/PyQt) Experience with GitLab CI/CD tools for software testing and deployment Experience using and creating Docker files for reproduceable
-
. Desired Skills: Experience with two-phase/multi-phase flows as evidenced by their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX
-
Integration, Simulation & Training, Network Programming (UDP/TCP/ZeroMQ), API Development, CUDA programming, python programming, Hardware Acceleration, strong verbal and written communication skills
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 7 hours ago
(e.g., bash scripting, python, Fortran, C++ and CUDA ), expertise in computational modeling (such as Molecular Dynamics, virtual screening, free energy calculations and Deep Learning), successful
-
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
-
posséder de solides compétences en calcul scientifique et HPC, notamment en : * Programmation en C++ dans des environnements parallèles (MPI, threads) ; * Programmation GPU (CUDA, HIP ou équivalent