Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- Oak Ridge National Laboratory
- CNRS
- Argonne
- Duke University
- NEW YORK UNIVERSITY ABU DHABI
- Technical University of Munich
- Texas A&M University
- Aarhus University
- Harvard University
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- SUNY Polytechnic Institute
- University of Miami
- AI4I
- Aalborg Universitet
- Aalborg University
- Brookhaven National Laboratory
- Chalmers University of Technology
- Dublin City University
- ELETTRA - SINCROTRONE TRIESTE S.C.P.A.
- ETH Zürich
- Eindhoven University of Technology (TU/e)
- FAPESP - São Paulo Research Foundation
- Forschungszentrum Jülich
- National Aeronautics and Space Administration (NASA)
- Northeastern University
- Norwegian Meteorological Institute
- Rutgers University
- Sandia National Laboratories
- Stanford University
- Technical University of Denmark
- University of Basel
- University of Jyväskylä
- University of Luxembourg
- University of Nebraska Medical Center
- University of New Hampshire – Main Campus
- University of North Carolina at Chapel Hill
- University of Turku
- University of Utah
- Université côte d'azur
- VIB
- 31 more »
- « less
-
Field
-
Master’s theses Requirements: Master’s or PhD degree with above-average results in Applied Maths (analysis, numerics, modeling) or in a comparable program with a strong math. focus and knowledge in
-
: Knowledge on floating point arithmetic and mixed/reduced precision computing techniques Experience with programming GPUs and/or other accelerators Proficiency in mathematical reasoning and numerical analysis
-
in top-tier machine learning/AI conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch
-
). 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
-
mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
-
managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken
-
programming (Shared and Distributed memory, GPU programming etc.) Demonstrated experience with distributed memory MPI programming Experience with collaborative software design, development, and testing
-
Apr 2026 - 00:00 (UTC) Country United Arab Emirates Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
-
++ Knowledge of GPU programming. Experience in application performance profiling. Desired qualifications: Experience from working on quantum computing Experience in FPGA development. Contract terms This postdoc
-
programming skills in C++ and/or Python; experience with high-performance or real-time computing, e.g. GPU, multi-core, embedded, is desirable Prior experience with SOFA is a clear advantage; experience with