Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- KINGS COLLEGE LONDON
- Princeton University
- University of Exeter
- University of Michigan
- University of Texas at Austin
- Lawrence Berkeley National Laboratory
- Monash University
- Nature Careers
- New York University
- Simons Foundation
- University of California
- University of Glasgow
- University of Helsinki
- University of Maryland, Baltimore
- University of Michigan - Ann Arbor
- University of Minho
- 8 more »
- « less
-
Field
-
development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
-
familiarised with the hydrodynamics of circumstellar discs, have the skills to run and adapt hydrodynamical simulations to be run in remote CPU/GPU clusters, and ideally have some experience producing synthetic
-
. Ability to succeed in an interdisciplinary team and communicate results clearly in writing and presentations. Desired Qualifications: Knowledge of GPU architecture and GPU programming. Interest
-
these codes in C++ or Fortran Adopting these codes for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in
-
computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224 CPU cores. We analyze large public datasets
-
communicate results clearly in writing and presentations. Desired Qualifications: Knowledge of GPU architecture and GPU programming. Interest or experience in distributed training on large scientific datasets
-
, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding
-
skills to run and adapt hydrodynamical simulations to be run in remote CPU/GPU clusters, and ideally have some experience producing synthetic observations of discs using radiative transfer software
-
person will focus on either using and/or developing Vlasiator. Prior knowledge in at least one of the following areas is required: GPU technologies, high-performance computing, parallelisation algorithms
-
development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D