Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- Nantes Université
- Aix-Marseille University
- CEA-Saclay
- Consortium Virome@tlas
- Ecole Normale Supérieure de Lyon
- FRANCE ENERGIES MARINES
- IMT - Atlantique
- INSA Strasbourg
- INSERM U1183
- Institut Pasteur
- University of Lille
- Université de Bordeaux / University of Bordeaux
- l'institut du thorax, INSERM, CNRS, Nantes Université
- 5 more »
- « less
-
Field
-
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
-
multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
-
, France [map ] Subject Areas: Statistics Machine Learning / Machine Learning Probability Mathematics Statistical Physics Appl Deadline: 2025/12/20 11:59PM (posted 2025/11/25, listed until 2026/05/25
-
Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques
-
, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
-
computer scientist with experience in bioinformatics, solid programming skills and knowledge in 3D protein structures. Machine learning skills and knowledge of Web development are a plus. Good interpersonal
-
Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 12 days ago
biology. The team is growing and offers a highly interdisciplinary environment that brings together researchers in structural bioinformatics, computational chemistry, biophysics, and machine learning. We
-
dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
-
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
-
Eligibility criteria Instrumental optics and imaging (microscopy, camera detection) for biology. Skills in coding and experiment control. Basics of machine learning and/or signal processing. Teamwork