Sort by
Refine Your Search
-
Listed
-
Employer
- Oak Ridge National Laboratory
- Argonne
- Northeastern University
- Stanford University
- University of California
- Yale University
- Lawrence Berkeley National Laboratory
- University of Kansas
- University of North Carolina at Chapel Hill
- Embry-Riddle Aeronautical University
- Georgia State University
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology (MIT)
- Nature Careers
- New York University
- The University of North Carolina at Chapel Hill
- University of Massachusetts Medical School
- University of New Hampshire
- University of New Hampshire – Main Campus
- 9 more »
- « less
-
Field
-
astrophysical free boundaries. Responsibilities include running high-resolution GPU-accelerated simulations on exascale computing systems, developing and applying geometric measure theory tools to quantify
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
. The postdoctoral scholar will be expected to improve on existing GPU-accelerated ocean models and develop laboratory experiments (in the Joint Fluids Lab at UNC), analyze results, publish in peer-reviewed journals
-
RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
-
modeling, or ordinary/stochastic differential equations. Experience in computational, statistical, or machine learning method development in any discipline. Experience in GPU computing frameworks (e.g
-
including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
-
that address real-world challenges and deliver positive business outcomes. The Institute for Insight is equipped with a computer cluster that includes multiple GPUs, designed for big data analytics for both
-
(e.g. systems biology), or ordinary/stochastic differential equations. Experience in computational, statistical, or machine learning method development in any discipline. Experience in GPU computing
-
environments, cloud computing, or GPU-accelerated machine learning Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation Familiarity with biological sequence alignment
-
in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
-
, with PyTorch and/or other GPU programming tools is also necessary. You should have completed all requirements for your PhD by the time you are hired. How to Apply: Candidates who have most, but not all