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obtained in monkeys on implicit statistical learning within our laboratory. • Mastery and adaptation of bio-inspired Hebbian learning models • Evaluation of the ability of these models to account for data
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Candidate Profile Training and Skills required (Recent) PhD in bioinformatics, statistics, or computer science with knowledge and interest in biology Track record of creativity in developing analytic
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Additional Information Eligibility criteria Knowledge : - Very good knowledge in Python - Good knowledge in statistics - Expertise in VHE high-level analysis - Knowledge in VHE gamma-ray and neutrino detectors
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, time-frequency analyses, statistical analyses and modelling required to link neural characteristics to decision-making processes (during a cost-benefit task involving monetary rewards and physical effort
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PyTorch. ✔️ You have a good knowledge of linear algebra and statistics. ✔️ You have good listening, analysis and synthesis skills, and are curious and open-minded. ✔️ You are adaptable, autonomous, rigorous
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; •Strong skills in applied econometrics and individual-level data analysis; •Proficiency in a statistical software package (Stata, R or equivalent); •Research interest in migration, citizenship, and
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, clinical and histological data in a translational framework. Main activities: - Bioinformatic analysis of WES and RNA-seq data. - Somatic variant detection and annotation. - Statistical and clinical
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. The research activity covers a broad spectrum of topics in mathematics, including a strong team in probability and statistics. The successful candidate will work at Dieudonné Mathematics Laboratory. Candidates
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new pollen analysis • Modeling and Statistics: Utilize GIS, R statistical environment, and other tools for pollen-based modeling (REVEALS and LOVE models) and statistical analysis. • Collaboration: Work
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, France [map ] Subject Areas: Machine Learning / Machine Learning Mathematics Probability Statistical Physics Statistics Appl Deadline: 2025/12/21 04:59 AM UnitedKingdomTime (posted 2025/11/25 05:00 AM