Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of North Carolina at Chapel Hill
- Argonne
- European Space Agency
- Imperial College London
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Nature Careers
- Oak Ridge National Laboratory
- Stony Brook University
- Technical University of Denmark
- Yale University
- ; Queen Mary University of London
- Brookhaven Lab
- Duke University
- Durham University
- Embry-Riddle Aeronautical University
- European Magnetism Association EMA
- Harvard University
- Heriot Watt University
- NEW YORK UNIVERSITY ABU DHABI
- Northeastern University
- Technical University of Munich
- The Ohio State University
- The University of Arizona
- UNIVERSITY OF HELSINKI
- University of Houston Central Campus
- University of Lund
- University of Minnesota
- University of Minnesota Twin Cities
- University of North Texas at Dallas
- University of South Carolina
- University of Texas at Arlington
- VIB
- 22 more »
- « less
-
Field
-
Dr Edward Johns, as well as a larger team across the UK as part of the ARIA-funded Robot Dexterity programme (see: https://www.aria.org.uk/opportunity-spaces/smarter-robot-bodies/robot-dexterity
-
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
-
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
-
libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver
-
for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations. This advancement will enable
-
, 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
-
. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second
-
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
-
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