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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges
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vision researchers to design algorithms specifically tailored for the extraction and analysis of these historical diagrams. EIDA considers these diagrams both as visual heritage and as tools
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people, including researchers, PhD students, engineers and technicians, within a multicultural and international environment. The person recruited will join the DYNAMOP group (https://www.ibs.fr/en
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diamond-anvil cell laboratory, a large-volume press laboratory (piston-cylinder, Paris-Edinburgh presses, multi-anvil press), a femtosecond laser micro-machining facility, electron microscopes (SEM, TEM
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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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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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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
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ExperienceNone Additional Information Eligibility criteria The postdoc should have a PhD degree in evolutionary biology, with expertise in bioinformatics, statistics, programming and/or modeling. Previous
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LanguagesFRENCHLevelBasic Research FieldMathematicsYears of Research ExperienceNone Research FieldHistory » History of scienceYears of Research ExperienceNone Additional Information Eligibility criteria PhD in mathematics
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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