Sort by
Refine Your Search
-
Listed
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- University of Luxembourg
- Université Grenoble Alpes
- AMPÈRE INSA-LYON
- CEATECH
- CESI LINEACT Strasbourg et CEREMA Strasbourg
- Idiap Research Institute
- Laboratoire Modélisation et Simulation Multi Echelle UMR 8208
- Télécom Paris
- Universidad de Sevilla
- University Gustave Eiffel
- University of Lille
- Université Savoie Mont Blanc
- Université Sorbonne Paris Nord
- Université de Limoges
- Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
- 7 more »
- « less
-
Field
-
SEMIAMOR "Machine Learning for Column Generation" See detailed description at https://lipn.fr/~leroux/static/phd_semiamor_cg.pdfLaboratory: Laboratoire d'Informatique de Paris Nord Field of
-
expertise in developing computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023
-
counting, or similar areas. Additional experience in the theory and practice of machine learning would be an asset. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8188-MARHEC-007
-
Inria, the French national research institute for the digital sciences | Grenoble, Rhône-Alpes | France | 9 days ago
specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus. Languages FRENCH Level Basic Languages ENGLISH Level Good Additional
-
will investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable
-
relies on three pillars: Optimal control theory , Machine learning and neural networks, Closed-loop control theory. While optimal control tools enable the description of optimal collision avoidance
-
University. IGE website: https://www.ige-grenoble.fr Activities Analyse and harmonise MOSAiC data to investigate discrepancies between observations and Arctic ozone simulations Use machine learning
-
models: deep learning (4D CNNs, Transformers, RNNs) on fMRI and ECoG data Explanatory AI: Applying machine learning models to guide post-stroke neurorehabilitation Predictive AI: Optimizing deep
-
will also support the development of machine-learning models to predict biochar catalytic performance from feedstock composition and preparation conditions. Where to apply Website https
-
Information Additional comments Candidate Profile and Required Skills: Background in AI: Strong academic background or training in artificial intelligence and machine learning / deep learning. Technical