Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Oak Ridge National Laboratory
- Argonne
- Nature Careers
- CNRS
- Duke University
- NEW YORK UNIVERSITY ABU DHABI
- Aarhus University
- Forschungszentrum Jülich
- Stanford University
- Technical University of Munich
- Harvard University
- New York University
- Rutgers University
- SUNY Polytechnic Institute
- Technical University of Denmark
- University of Luxembourg
- University of Miami
- University of Nebraska Medical Center
- University of North Carolina at Chapel Hill
- AI4I
- Brookhaven National Laboratory
- Chalmers University of Technology
- Czech Technical University in Prague
- ELETTRA - SINCROTRONE TRIESTE S.C.P.A.
- ETH Zürich
- Eindhoven University of Technology (TU/e)
- Erasmus MC (University Medical Center Rotterdam)
- FAPESP - São Paulo Research Foundation
- Flanders Institute for Biotechnology
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- ICN2
- Max Planck Institute for Solar System Research, Göttingen
- Max Planck Institute of Animal Behavior, Radolfzell / Konstanz
- McGill University
- Nagoya University
- Northeastern University
- RIKEN
- Sandia National Laboratories
- Texas A&M University
- Umeå universitet stipendiemodul
- University of Basel
- University of Central Florida
- University of Florida
- University of Jyväskylä
- University of New Hampshire
- University of Turku
- University of Utah
- Utrecht University
- VIB
- 39 more »
- « less
-
Field
-
). 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 AI methods as
-
E13) up to 5 years International collaboration to build a large radiotherapy dataset Dedicated GPU infrastructure Strong collaborations within TUM’s AI ecosystem High-impact publication potential
-
). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
-
projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
-
mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
-
finite-element models, e.g. Poisson, linear elasticity, large-deformation soft tissue, for real-time execution on AR devices and GPUs Implement these models within open-source frameworks such as SOFA
-
infrastructure, providing trainees with access to UF’s HiPerGator supercomputing facility, including 50 NVIDIA B200 GPUs, and a high-throughput automated screening platform. We offer a supportive, collaborative
-
engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum
-
. This project seeks to overcome key workflow and precision limitations in HDR brachytherapy by enabling real-time adaptive optimization during needle insertion, integrating live ultrasound imaging with GPU
-
clusters, cloud computing, or GPU acceleration. Strong mathematical background in linear algebra, probability, and statistics. Prior research experience with publications or preprints. The University