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relevant to the project's theme and activities. Solid experience in molecular simulation and/or machine learning is required, along with a good knowledge of associated theoretical tools (experience in
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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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
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 2 months ago
framework that maximizes sensitivity to the targeted model parameters. In addition, one could also study the separation of partial data, for instance using learning techniques. The applicant will review
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be the continuation of previous work, would use machine learning on a simulated data base to define the tool, followed by an application to real data from GRAVITY/VLTI (K band), MATISSE/VLTI (L, M, N
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Description Conduct a part of the ANR MetaTime (setting-up experiments, acquisition and processing of data, writing scientific reports) • Perform a review of the existing litterature on the topics • Acquire
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Institut of Mathematics of Marseille | Marseille, Provence Alpes Cote d Azur | France | 2 months ago
machine learning applications. Position Objective : The primary focus of this position is to develop concentration inequalities in the nonstationary setting, specifically for periodic Markov chains and
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analysis and visualization, signal processing, and ideally machine learning. • Working knowledge of Distributed Acoustic Sensing (DAS) and its applications in seismology (appreciated). • Aptitude
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visualization. Experience with GWAS, Bayesian modelling, and/or machine learning applied to biological data. Strong programming skills (R, Python) and ability to manage large-scale -omics datasets. Good
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in the area of scientific computing and Computational Fluid Dynamics. Prior Experience in turbulence modelling, machine learning or the Lattice Boltzmann method is an advantage. Operational skills