Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- CNRS
- IMT Atlantique
- Inria, the French national research institute for the digital sciences
- ONERA
- Centrale Lille Institut
- Ecole Centrale de Lyon
- Ecole Nationale des Ponts et Chaussées (ENPC)
- Ecole des Mines de Saint-Étienne
- Gisèle Krysztofiak
- IMT MINES ALES
- IMT Mines Ales
- INSA Strasbourg
- LAUM UMR CNRS 6613
- Nature Careers
- Télécom Paris
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- Université Claude Bernard Lyon 1
- Université Gustave Eiffel
- Université Paris Cité
- Université d'Orléans
- Université de Montpellier
- fluiidd
- École nationale des ponts et chaussées
- 13 more »
- « less
-
Field
-
9 Mar 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Etude des Microstructures et de Mécanique des Matériaux Research Field Engineering Physics » Acoustics Engineering
-
spray-drying and freeze-drying, linking molecular/colloidal interactions in the feed formulation to process-driven microstructure, and ultimately to stability and release kinetics. The work will deliver
-
. Computer science, ranging from classical programming to computational tools for engineers (data models, numerical computation, simulation, optimization), is an integral part of the curriculum. In recent years
-
direction investigates the role of attention mechanisms in event-driven and spike-based models. Transformer architectures will serve as a reference framework to analyze the benefits and limitations
-
Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 1 month ago
demonstrated the efficacy of employing machine learning approaches to furnish engineers with a rapid computational asset for structural design and monitoring. Given that numerical models incorporating multi
-
–12): State of the art and system modeling This phase will focus on an in-depth literature review on mobility management in satellite and non-terrestrial networks, as well as AI-driven networking
-
leveraging a large-scale multicentric dataset and advanced computational modeling, this project seeks to move beyond traditional prognostic markers and to establish reproducible, imaging-driven predictors
-
collective dissipative dynamics in quantum emitter systems (such as cold atoms) strongly coupled to a driven cavity, while accounting for motion-related degrees of freedom. The focus will be on developing
-
SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
-
engineering or related fields. Strong background in fluid mechanics, scientific computing, numerical methods, PDEs, and/or data-driven modeling Interest in interdisciplinary research and open science