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 New Hampshire – Main Campus
- University of North Carolina at Chapel Hill
- Embry-Riddle Aeronautical University
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology (MIT)
- Nature Careers
- Rutgers University
- The University of Arizona
- The University of North Carolina at Chapel Hill
- University of Massachusetts Medical School
- University of Utah
- 9 more »
- « less
-
Field
-
developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
-
the computer science research conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings
-
Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
NASA's Jet Propulsion Laboratory, focused on developing a next-generation, GPU-based climate model that learns physics from data to improve the accuracy of its projections. Will collaborate with
-
Jet Propulsion Laboratory, focused on developing a next-generation, GPU-based climate model that learns physics from data to improve the accuracy of its projections. Will collaborate with oceanographers
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours 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
-
reasoning or tool-augmented LLMs, RL (RLHF/RLAIF/online RL), or foundation models for science, Software engineering skills (Python) and experience with modern DL stacks (PyTorch) and multi-GPU training
-
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
-
(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