Sort by
Refine Your Search
-
Country
-
Program
-
Employer
- California Institute of Technology
- Ecole Centrale de Lyon
- Oak Ridge National Laboratory
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Nanyang Technological University
- Nature Careers
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Kansas
- Aalborg University
- CNRS
- Center for Theoretical Physics PAS
- Central China Normal University
- Delft University of Technology (TU Delft); yesterday published
- INSTITUTO DE ASTROFISICA DE CANARIAS (IAC) RESEARCH DIVISION
- Lawrence Berkeley National Laboratory
- Umeå universitet stipendiemodul
- University of California
- University of Cambridge;
- University of Southern California
- University of Southern California (USC)
- Virginia Tech
- 11 more »
- « less
-
Field
-
and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
-
project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational
-
from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with experimental data from both literature and in
-
-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational fluid dynamics, material
-
completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
-
advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
-
steady and transient state, at scales ranging from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with
-
of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
-
Computational Fluid Dynamics. Operational skills : Physical analysis of fluid dynamics, advanced skills in programming and numerical methods, writing scientific reports and articles, presenting at scientific
-
of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity