Sort by
Refine Your Search
-
Country
-
Program
-
Employer
- Forschungszentrum Jülich
- CNRS
- FAPESP - São Paulo Research Foundation
- IMEC
- LINKS Foundation - Leading Innovation & Knowledge for Society
- Northeastern University
- Oak Ridge National Laboratory
- Sandia National Laboratories
- Singapore-MIT Alliance for Research and Technology
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- Universidad Politecnica de Cartagena
- University of Basel
- University of Bergen
- University of Oslo
- University of Utah
- University of Washington
- Washington University in St. Louis
- 7 more »
- « less
-
Field
-
to significant losses during processing, transport, retail and storage stages. In parallel, advances in sensor, information and communication technologies, together with increased computational capabilities, have
-
engineering; Formal methods, models, and languages; Interactive and cognitive systems; Distributed systems, parallel computing, and networks. The successful candidate will work closely with teams specializing
-
, nuclear and biomedical fusion through the experimental laboratories of rapid prototyping, biomedical and photonics. In the field of High Performance Computing (HPC), activities related to parallel
-
, or deployment at scale. A proven track record of high-quality research contributions published in top-tier machine learning conferences or journals. Proficiency in high-performance computing, distributed and
-
through sensitivity, uncertainty, and scalability analyses. – Enhance the computational efficiency of large-scale optimization problems by exploring decomposition techniques, parallelization, and
-
hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
-
results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
-
software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
-
programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers