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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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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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. Good experience in data analysis, knowledge of statistical techniques for high energy physics ex-periments, and an interest in detector development are required. Previous experience with the analysis
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, statistical and case of study of switchbacks. Comparaisons with synthetic observations from the dedicated simulations of the JET2SB project. 2) Collaboration with the local LPC2E team, as well as the full
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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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the postdoctoral fellowship. • License to practice psychology and clinical experience in hospital-based neuropsychology. • Experience with and proficiency in inferential statistics in neuroscience. • Analytical and
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a modeling approach inspired by statistical physics to describe individual strategies, their interactions, and emergent effects at the group scale. The candidate will contribute to the development and
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degree in aquatic microbial ecology and experience in high-throughput sequencing data analysis and statistical tools (familiarity with the UNIX terminal environment; management of big data on clusters
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- Programming skills (e.g., Python, Matlab) - Advanced knowledge of statistical analysis (e.g., R) Desirable skills: -Specialization in language development, including research experience with infants and/or
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statistical models. Within the Polarity, Division and Morphogenesis team, the candidate will work closely with biologists and physicists to develop approaches integrating spatial transcriptomics, cell dynamics