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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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machine learning tools. The postdoctoral fellow will contribute to various aspects of the project, such as: * developing new theoretical and numerical approaches for determining the thermodynamic and
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interest: Advanced techniques for data storage/retrieval/processing/visualization on large scale. Cybersecurity Software engineering Machine/deep learning Technical aspects of human computer interaction (HRI
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. - Knowledge in programming, data treatment, electron diffraction simulations, mathematical skills, knowledge about machine learning and artificial intelligence is a plus. Website for additional job details
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skills (one or more of the following strongly desired) Exploratory analysis of massive datasets (machine learning methods) Spatial data analysis and Geographic Information Systems (GIS) Forecasting and
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repeatability and accelerate the generation of relevant experimental data for machine learning. Numerical component The research engineer will contribute to the enrichment of an existing database, relying
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well as in the design of machine learning algorithms (ANN, SVM, Decision Tree, and Random Forest) applied to healthcare, will be particularly valued. Proficiency in programming tools (Matlab) and statistical
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for model creation and operation Write scientific articles Supervisory duties Number of staff supervised Category C : 1 Ability to integrate into a project involving more than ten people Computer languages
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information extraction from images or videos, object detection and tracking techniques. - Machine learning and artificial intelligence: mastery of supervised and unsupervised methods (CNN, clustering
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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates