Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Oak Ridge National Laboratory
- Forschungszentrum Jülich
- Argonne
- CNRS
- Duke University
- Harvard University
- Inria, the French national research institute for the digital sciences
- Nature Careers
- University of Glasgow
- Université de Montpellier
- Aarhus University
- Blekinge Institute of Technology
- Brookhaven National Laboratory
- Centrale Lille Institut
- Chalmers University of Technology
- Cranfield University
- DENA DESARROLLOS SL
- ETH Zurich
- Ecole Centrale de Lyon
- IFREMER - Institut Français de Recherche pour l'Exploitation de la MER
- LEM3
- Lawrence Berkeley National Laboratory
- Le Mans Université
- MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Macquarie University
- Michigan State University
- Swansea University
- Tallinn University of Technology
- UiT The Arctic University of Norway
- Umeå universitet stipendiemodul
- Universitaet Muenster
- University of Arkansas
- University of Central Florida
- University of Colorado
- University of North Carolina at Chapel Hill
- University of Oslo
- University of Portsmouth;
- University of Sheffield
- University of Texas at Austin
- Université de Lille
- cnrs
- 32 more »
- « less
-
Field
-
-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
-
. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
-
chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU
-
, you’ll join a technically driven, publication active team known for research in computational modelling, CFD, numerical methods and high-performance computing, with a strong culture of code quality, open
-
learning architectures for scientific or high-performance computing applications. Background in software performance evaluation, profiling, and optimization on CPUs and GPUs. Knowledge of common numerical
-
). 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
-
validation experiments for modelling • Computational fluid dynamics techniques • Finite element analysis method • Reviewing literature, planning and managing research, writing technical report / paper
-
for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
-
five years. Demonstrated expertise in computational mechanics and numerical modeling Experience in polymer composite manufacturing processes Experience with simulation tools for thermomechanical analysis
-
edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations