Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Oak Ridge National Laboratory
- EPFL
- Argonne
- Stanford University
- AALTO UNIVERSITY
- Computer Vision Center
- Forschungszentrum Jülich
- King's College London
- MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE
- NEW YORK UNIVERSITY ABU DHABI
- SciLifeLab
- University of North Carolina at Chapel Hill
- Utrecht University
- Yale University
- ;
- Aarhus University
- Academia Sinica
- Baylor College of Medicine
- Eindhoven University of Technology (TU/e)
- European Space Agency
- Fundació per a la Universitat Oberta de Catalunya
- Indiana University
- Inria, the French national research institute for the digital sciences
- Istituto Italiano di Tecnologia
- KTH Royal Institute of Technology
- KU LEUVEN
- Lehigh University
- Max Planck Institute for Gravitational Physics, Potsdam-Golm
- Mohamed bin Zayed University of Artificial Intelligence
- National Aeronautics and Space Administration (NASA)
- Northeastern University
- Pennsylvania State University
- SUNY University at Buffalo
- SUNY at Buffalo
- Sandia National Laboratories
- Singapore-MIT Alliance for Research and Technology
- Stony Brook University
- Texas A&M University
- The Ohio State University
- The University of Arizona
- Toyota Technological Institute
- University of California
- University of California, Los Angeles
- University of Central Florida
- University of Florida
- University of Lund
- University of Minnesota
- University of South Carolina
- University of Utah
- Uppsala universitet
- 40 more »
- « less
-
Field
-
(e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative
-
candidates working in formal methods and/or computer systems with interests in one or more of the following areas: machine-checked verification of systems software, GPU kernel semantics and verification, and
-
systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python, using modern practices for software development, testing, documentation, and version control
-
Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 14 days ago
group. Our offer Computing The department operates Urania, a high-performance cluster with ~6,050 cores; Hypatia, a high-throughput cluster with ~12,000 cores; and two GPU servers, Saraswati and Lakshmi
-
laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical
-
specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as well as access to the National High-performance Computing Center. ASIAA is
-
Dask and NetCDF or Zarr datasets. Experience with GPU or distributed training and familiarity with agricultural or environmental applications of weather forecasts. Experience working in interdisciplinary
-
extracting transport properties from molecular dynamics trajectories. ● Experience with GPU-accelerated machine learning frameworks (for example CUDA, PyTorch, or GPU-enabled LAMMPS). ● Experience
-
applications for scientific, agricultural, environmental, geospatial, or cyber-physical systems. Experience with GPU computing, distributed training or inference, high-performance computing systems, cloud-native
-
— shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and iterative linear solvers — communication-avoiding methods, sparse LU