Sort by
Refine Your Search
-
Listed
-
Employer
- Argonne
- University of North Carolina at Chapel Hill
- Oak Ridge National Laboratory
- Duke University
- Stony Brook University
- University of South Carolina
- Yale University
- Brookhaven Lab
- Embry-Riddle Aeronautical University
- Emory University
- Harvard University
- New York University
- Northeastern University
- Stanford University
- The Ohio State University
- The University of Arizona
- University of Colorado
- University of Minnesota
- University of North Texas at Dallas
- University of Texas at Arlington
- 10 more »
- « less
-
Field
-
mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
-
techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as
-
models (e.g., CNNs, diffusion models, etc) Proficiency in Python Experience with HPC (CPU or GPU, with GPUs preferred) Related Skills and Other Requirements Ability to collaborate on the application of AI
-
computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
-
Mathematica and Python with an interest in GPU programming. These required and desired skills should be demonstrated by presenting an existing body of code and/or peer-reviewed publications. Additional
-
turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
-
, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
-
, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
-
, 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
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC-Chapel Hill, Duke University, and North Carolina State University all