Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at
-
atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
-
, or ability to learn quickly) and/or symbolic/numerical computation (Mathematica, R, Python). • Fluency in scientific English (reading, writing, speaking) is essential to interact with the project's
-
Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
-
to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
-
stability analysis and control, machine learning, dimensionality reduction and high-performance computing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3346-NADMAA-159/Default.aspx
-
for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
-
, CADENA seeks to replace toxic catalysts with eco-friendly, high-performance alternatives, aligning with France's and Europe's green chemistry and circular economy goals. Where to apply Website https
-
condensed matter physics • Ability to learn and develop skills in analytical computation, theoretical modelling and numerical simulations, in particular the numerical solution of partial differential
-
MPC combining continuous controls (curtailment, storage) and discrete topology switching. • Develop scalable solvers and heuristics (relaxations, decomposition, learning-assisted policies) for near-real