Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- NEW YORK UNIVERSITY ABU DHABI
- Princeton University
- Argonne
- Nature Careers
- University of North Carolina at Chapel Hill
- Barnard College
- California Institute of Technology
- European Space Agency
- Oak Ridge National Laboratory
- Duke University
- Nanyang Technological University
- National University of Singapore
- Stony Brook University
- Technical University of Denmark
- University of Luxembourg
- University of South Carolina
- Yale University
- ;
- ; University of Oxford
- Brookhaven Lab
- Carnegie Mellon University
- Durham University
- Embry-Riddle Aeronautical University
- Emory University
- Empa
- European Magnetism Association EMA
- Forschungszentrum Jülich
- Harvard University
- Imperial College London
- Jane Street Capital
- Linköping University
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Manchester Metropolitan University
- New York University
- Northeastern University
- Shanghai Jiao Tong University
- Simons Foundation
- Stanford University
- Technical University of Munich
- The Ohio State University
- The University of Arizona
- UNIVERSITY OF HELSINKI
- UNIVERSITY OF SOUTHAMPTON
- UNIVERSITY OF SURREY
- University of Antwerp
- University of Colorado
- University of Maryland, Baltimore
- University of Massachusetts
- University of Minnesota
- University of New Hampshire – Main Campus
- University of North Texas at Dallas
- University of Texas at Arlington
- University of Texas at Austin
- VIB
- 44 more »
- « less
-
Field
-
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
-
Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
-
managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
-
, TensorFlow) with several years of practice Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning
-
. 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
-
implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
-
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
-
library research and field explorations. Run experiments, analyze results, and prepare research outputs. Execute large-scale training jobs on GPU clusters, track metrics, visualize findings, and contribute
-
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
-
needs, such as assisting the team with evaluating evolutionary algorithms for exploring creative new hand designs, or reinforcement learning for policy optimisation, all within a huge GPU-based simulation