Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- CNRS
- Nature Careers
- Inria, the French national research institute for the digital sciences
- Institut Pasteur
- Aix-Marseille Université
- CEA
- The American University of Paris
- Université de Technologie de Belfort-Montbéliard
- Arts et Métiers Institute of Technology (ENSAM)
- BRGM
- Consortium Virome@tlas
- Ecole Centrale de Lyon
- Ecole polytechnique
- FEMTO-ST institute
- French National Research Institute for Sustainable Development
- ICMMO
- INSERM U1183
- IRISA
- Institut Curie - Research Center
- Institut of Mathematics of Marseille
- Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique,
- Observatoire de la Côte d'Azur
- University of Paris-Saclay
- Université Côte d'Azur
- Université Grenoble Alpes
- Université Paris-Saclay (UPS)
- Université Paris-Saclay GS Mathématiques
- Université de Caen Normandie
- École Normale Supéireure
- École nationale des ponts et chaussées
- 20 more »
- « less
-
Field
-
results in leading conferences and journals Required Qualifications PhD in one of the following areas (or related fields): Machine learning / deep learning Quantum computing / quantum information Applied
-
. The candidate must be able to communicate in English (oral and written). The knowledge of the French language is not required. The candidate must have a strong interest in machine learning. Skills in
-
, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
-
: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM
-
properties changes. - The demonstration of the tear detection with machine learning classification applied directly on S-parameters of the MWI system without solving the inverse problem. The objective
-
. The objective of this thesis project is to develop hybrid models that integrate electrochemical principles with machine learning techniques to analyze data from electrolyzers, predict performance, assess lifespan
-
parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
-
of the art data science approaches (text mining, machine learning, AI) to comprehensively highlight yet undiscovered virus/host/environment relationships and annotate potentially putative new spillover
-
by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally
-
algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation