Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- Ecole Normale Supérieure de Lyon
- Nantes Université
- Universite de Montpellier
- Université Claude Bernard Lyon 1
- Université de Bordeaux / University of Bordeaux
- Aix-Marseille University
- CEA-Saclay
- Fondation Nationale des Sciences Politiques
- Grenoble INP - Institute of Engineering
- IFP Energies nouvelles (IFPEN)
- IMT - Atlantique
- IMT Mines Albi
- INSA Strasbourg
- Institut Pasteur
- Nature Careers
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- Université Grenoble Alpes
- Université de Strasbourg
- l'institut du thorax, INSERM, CNRS, Nantes Université
- 11 more »
- « less
-
Field
-
are looking for a motivated postdoc with solid experience in bioinformatics and in machine learning. The position centers on SRPs and LLPS, and the successful candidate will contribute to several ongoing
-
at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect
-
team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS
-
Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
-
, involving expertise in optics, electronics, image and data processing, chemistry, and biology. With the support of several European funding programs, the team is building a data science and machine learning
-
Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques
-
conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
-
develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
-
on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
-
Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a