Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
Inria, the French national research institute for the digital sciences | Grenoble, Rhône-Alpes | France | 15 days ago
algorithms, participate in the development and maintenance of the PEPit ( https://pepit.readthedocs.io/ ) software package, including adding functionalities related to parallel optimization
-
Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 15 days ago
-pipeline, constituted from the [killMS]( https://github.com/saopicc/killMS ) stage and the [DDFacet]( https://github.com/saopicc/DDFacet ) stage, both developed in particular by Cyril Tasse from the Paris
-
and application of the AgroC agroecosystem model for simulating water, carbon, nutrient and energy fluxes at the field and farm scale Integration of AgroC with the Parallel Data Assimilation Framework
-
for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing
-
the thesis will investigate the biological response in vitro, including cell adhesion and proliferation, using different cell types. In parallel, part of the project will focus on investigating the
-
methodological focus will be the precise determination of the number of confined water molecules, addressed through two parallel strategies: connecting the channel to a fixed-pressure reservoir, and the
-
, August). ExaNBody: A HPC framework for n-body applications. In European Conference on Parallel Processing (pp. 342-354). [2] https://github.com/MarcJos/Merope [3] https://github.com/MarcJos/RSA_MPI
-
. 2022, 87, 9699-9713. https://doi.org/10.1021/acs.joc.2c00748 . Bruneau, V. et al. Chem. Eur. J. 2026, corrected proofs: https://doi.org/10.1002/chem.71696 Where to apply E-mail michel.boisbrun
-
will Identify new applications for Machine Learning in science, engineering, and technology Develop, implement and refine ML techniques Implement parallel ML training on the High Performance Computers
-
infer excitation conditions and quantify the impact of fast, tenuous shocks on ionized line emission. In parallel, high-resolution 3D MHD simulations of fast shocks interacting with dense interstellar