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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
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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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). An overview of recent multi-view clustering. Neurocomputing, 402, 148-161. 7. Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021). Machine learning for perturbational single-cell omics. Cell Systems, 12
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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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Institut of Mathematics of Marseille | Marseille, Provence Alpes Cote d Azur | France | about 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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Inria, the French national research institute for the digital sciences | Bures sur Yvette, le de France | France | 2 months ago
mathematical modeling (preferably physiological systems) and/or control theory. -Experience in signal processing and artificial intelligence methods (time-series analysis, machine learning, multimodal
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in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions in
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research and excellent digital literacy Strong interest in historical data, machine learning, data visualization, or digital hermeneutics Strong communication skills in English and good knowledge of French
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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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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP