Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of North Carolina at Chapel Hill
- Argonne
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Yale University
- Forschungszentrum Jülich
- Imperial College London
- NEW YORK UNIVERSITY ABU DHABI
- University of Luxembourg
- ; Technical University of Denmark
- Brookhaven Lab
- Duke University
- Embry-Riddle Aeronautical University
- Empa
- European Magnetism Association EMA
- European Space Agency
- Georgia State University
- Heriot Watt University
- INESC ID
- KINGS COLLEGE LONDON
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology (MIT)
- McGill University
- New York University
- Northeastern University
- Oak Ridge National Laboratory
- Reykjavik University
- Shanghai Jiao Tong University
- Stanford University
- Technical University of Munich
- The Ohio State University
- The University of Arizona
- University of Houston Central Campus
- University of Minnesota
- University of Minnesota Twin Cities
- University of North Texas at Dallas
- University of Oxford
- University of South Carolina
- University of Texas at Arlington
- 28 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
-
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. The role offers exceptional
-
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
-
, 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
-
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
-
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
-
https://scholar.google.com/citations?user=9IRAYdEAAAAJ& ;hl=en and https://www.physics.sjtu.edu.cn/amgg/ Research profile: Candidates with a previous background on GPU computing are especially encouraged
-
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
-
in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct