Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- AALTO UNIVERSITY
- Stony Brook University
- European Space Agency
- Oak Ridge National Laboratory
- Bucharest Universty of Economic Studies
- CNRS
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Technical University of Munich
- Argonne
- KINGS COLLEGE LONDON
- The University of Arizona
- University of Luxembourg
- Aarhus University
- Aix-Marseille Université
- Brookhaven Lab
- Brookhaven National Laboratory
- CISPA Helmholtz Center for Information Security
- Caltech
- Delft University of Technology (TU Delft); yesterday published
- ETH Zürich
- Ecole Centrale de Lyon
- IRTA
- King's College London
- Lawrence Berkeley National Laboratory
- Linköpings University
- Luleå University of Technology
- Nantes Université
- National Aeronautics and Space Administration (NASA)
- Northeastern University
- Texas A&M University
- The University of North Carolina at Chapel Hill
- Télécom Paris
- UNIVERSITY OF VIENNA
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- Universitatea Maritimă din Constanța
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); today published
- University of California
- University of North Carolina at Chapel Hill
- University of Oulu
- University of Southern Denmark
- University of Trás-os-Montes and Alto Douro
- University of Turku
- University of Utah
- University of Washington
- Université Grenoble Alpes
- Wageningen University & Research
- Washington University in St. Louis
- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
- Yale University
- 40 more »
- « less
-
Field
-
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 the concept of optimal transport for inverse problems. Optimal transport is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions
-
machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
-
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
-
5 Sep 2025 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Computer science Mathematics » Algorithms
-
. 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
-
control and energy management strategies, including centralized / distributed control approaches, for ESS coordination and ancillary service delivery. Develop optimization algorithms and Al-based methods
-
of Physics and Astronomy. AIPAD tackles the above questions by developing two innovative AI algorithms: The first algorithm will infer full SEP pitch-angle distributions (PADs) for spacecraft measurements
-
tomography (SPECT) is an imaging technique that tracks the 3D distribution of a radioactive tracer administered to a patient to monitor certain biological functions. The long acquisition times (10-40 minutes