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, such as spatial modeling and analysis, transport and port environments, health and risks, Information and Communication Technologies (ICT), and socio-territorial restructuring. Its identity is associated
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in ecological fieldwork, ideally in wet grassland or agricultural systems - Skills in GIS, remote sensing, and spatial data analysis (bonus: agent-based modelling) - Demonstrated ability to work in
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. The post-doctoral student will be responsible for implementing the display and visualisation of spatial and urban data, carrying out the study and evaluation of these representations, and contributing
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cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from statistical models. Within the Polarity
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-processing pipeline for high-field MRI medical data (normalization, denoising, spatial registration) to optimize the quality and consistency of data used in analyses, and to facilitate the search
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the nature of the ambient noise in the reactor (spectrum, spatial and temporal distributions, levels), the approach consists in developing passive methods by determining their conditions of use but not
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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput
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and Radiative Transfer models already developed as part of the ANR AGN_MELBa work. This might require running and adapting these models. Then, at a different spatial scale, the multi scale tools will be
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spatial scales. The laboratory is organized around five interacting research teams, whose scientific activities are rooted in the fields of sedimentology, geochemistry, climatology and paleoclimatology
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating