Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Argonne
- European Space Agency
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Oak Ridge National Laboratory
- Stony Brook University
- CNRS
- AALTO UNIVERSITY
- Technical University of Munich
- University of Luxembourg
- University of Washington
- Brookhaven Lab
- Brookhaven National Laboratory
- ETH Zürich
- Ecole Centrale de Lyon
- Heriot Watt University
- Nantes Université
- National Aeronautics and Space Administration (NASA)
- Technical University of Denmark
- Texas A&M University
- UNIVERSITY OF HELSINKI
- UNIVERSITY OF VIENNA
- Universitatea Maritimă din Constanța
- University of Trás-os-Montes and Alto Douro
- University of Turku
- Université Grenoble Alpes
- Vrije Universiteit Brussel
- Wageningen University & Research
- Wageningen University and Research Center
- Yale University
- 19 more »
- « less
-
Field
-
of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
-
differential problems. 2) Development of adaptive mesh generation algorithms for distributed order fractional differential equations. 3) Analysis of the stability and convergence properties of the developed
-
wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
-
is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions. The concept has lately gained increasing interest from
-
for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
-
wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
-
analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
-
trustworthy medical AI? Deep models already outperform humans on many benchmarks, yet in the clinic they remain black boxes: radiologists cannot see why an algorithm flags a lesion, and AI engineers cannot tell
-
. They come in various configurations, from simple, conceptual lumped models to more complex, distributed ones. Their low input data requirements and flexible application make them widely used by water managers
-
Navier-Stokes equations at a macroscopic level, the LB method considers the fluid at a kinetic level. Capturing the dynamics of collections of fluid particles distributed over a lattice is here preferred