Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- CNRS
- Nature Careers
- Ecole Centrale de Lyon
- Inria, the French national research institute for the digital sciences
- Centrale Supelec
- Grenoble INP - Institute of Engineering
- IFP Energies nouvelles (IFPEN)
- Institut Pasteur
- Institut polytechnique UniLaSalle
- Université Gustave Eiffel
- Université Toulouse Capitole
- Université côte d'azur
- 2 more »
- « less
-
Field
-
simulations. Two complementary strategies will be employed: structure-based virtual screening (docking simulations + molecular dynamics) and ligand-based virtual screening (machine learning models). We have
-
The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
-
Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
-
systems Experience in deep learning, computer vision, or multimodal data integration Exposure to federated learning, privacy preserving analytics, or distributed systems Knowledge of clinical data models
-
training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
-
perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
-
costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
-
Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 27 days ago
. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024 Kugler, F. Forbes, and S. Douté. Fast
-
combining machine learning and biophysical modelling to model embryo development from spatial transcriptomics data. Activities : - design of a new mathematical method - monitoring and study of publications
-
growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML