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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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-assisted mass spectrometry and vibrational spectroscopy. Advanced statistical analyses will be applied to link the information on the composition of the species, obtained by mass spectrometry, with
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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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, metabarcoding) - Bioinformatics analyses - Environmental data synthesis - Statistical analysis of data - Fieldwork participation (eDNA/eRNA sampling) - Scientific writing and communication - Supervision
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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
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skills in quantitative methods, statistics, and project management. Experience in dietary decision-making, eating disorders, or addiction/craving is considered a bonus. Additional comments -- Website