Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- Aix-Marseille Université
- Ecole Centrale de Lyon
- Grenoble INP - Institute of Engineering
- INRIA
- INSA Rennes
- Institut Pasteur
- LEM3
- LSPM , CNRS 3407
- Nantes Université
- Nature Careers
- Science me Up
- Sorbonne University
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- UTTOP
- Université Toulouse Capitole
- cnrs
- 8 more »
- « less
-
Field
-
investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
-
a major challenge, accounting for up to 50% of global electricity consumption by 2030. This situation is largely due to the Von Neumann computing architecture, which limits the energy efficiency
-
3D environments. • Design of a robust control architecture to ensure autonomous navigation using information from the optical localization system (development of estimation algorithms, use of observers
-
investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
-
LevelPhD or equivalent Skills/Qualifications - Excellent knowledge in architecture of quantized and pruned neural networks - Solid programming skills, languages C / C++ / Python - Solid knowledge in
-
-compatible measurement system using a coplanar waveguide architecture, assess their sensitivities, evaluate the possibilities of combining them, and test them with real biological samples—going beyond simple
-
-fidelity neural surrogates: residual learning and hierarchical neural architectures conditioned on fidelity indicators and latent variables. - Hybrid probabilistic--deep models: combine neural
-
the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
-
architecture combining perception, joint control, and quantitative evaluation of collaborative performance, with the objective of improving the fluency, safety, and efficiency of collaborative mobile
-
with SQL and basic database concepts Desirable: Experience with LLMs, NLP, embeddings, semantic search, or generative AI Familiarity with RAG architectures, vector databases, or knowledge-enhanced AI