Sort by
Refine Your Search
-
Employer
- Oak Ridge National Laboratory
- Argonne
- Harvard University
- Helmholtz Association of German Research Centres
- Stanford University
- Yale University
- Baylor College of Medicine
- Indiana University
- Lehigh University
- National Aeronautics and Space Administration (NASA)
- Northeastern University
- Pennsylvania State University
- Rutgers University
- SUNY University at Buffalo
- SUNY at Buffalo
- Sandia National Laboratories
- Stony Brook University
- Texas A&M University
- The University of Arizona
- University of California
- University of California, Los Angeles
- University of Florida
- University of Minnesota
- University of New Hampshire
- University of North Carolina at Chapel Hill
- University of South Carolina
- University of Utah
- 17 more »
- « less
-
Field
-
knowledge in parallel/GPU computing. Job Duties Job Duty Doing research problems in the area of mathematical foundations of data science and machine learning. The postdoc will assist with ongoing research
-
such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
-
National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 17 days ago
with rotorcraft aerodynamics, comprehensive analysis tools, or high-order unstructured solvers Experience with GPU-accelerated CFD workflows Point of Contact Mikeala Eligibility Requirements Degree
-
. Experience with GPU-based training and high-performance computing. Interest in translating methodological contributions into high-impact medical AI venues (e.g., Nature Medicine, Nature Machine Intelligence
-
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
-
scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
-
transformer architectures (e.g., ViT/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g
-
their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
-
conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
-
analysis tools (e.g., 3D Slicer) Experience managing GPU-enabled cluster nodes Desirable: Experience working with current large language models (LLMs) and their ecosystems Required Application Materials