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, the environment and ecology, transportation, robotics, energy, culture, and artificial intelligence. Overview of the CNRS as an employer: https://www.cnrs.fr/fr/le-cnrs Presentation of IRISA as the host laboratory
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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Department of Land Surveying and Geo-Informatics Research Assistant Professor in Geospatial Artificial Intelligence (GeoAI) / Climate Resilience / Urban Sustainability / Global Change Ecology
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of the microbial ecology results in mineralized environment in the petrology and thermodynamic modeling components of the project. This project involves 6 (teachers)-researchers and research engineers from
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Areas: Ecology and Evolutionary Biology Biology / Biodiversity , Biology , Biophysics , Cell & Molecular Neurobiology , Cell Biology , Climate-change Biology , Computational biology , Ecology
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, visit https://www.biocean5d.org/ . The applicants will work on close collaboration with Jose M. Montoya, from CNRS. - Developing analytical theory and/or simulation models on the relative importance
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FieldEnvironmental scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Required education level A Master’s degree in oceanography, ecology or modelling (required by the start date of the PhD
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systems. Key responsibilities Develop modeling tools to assess the environmental and socio-economic sustainability of industrial supply chains and processing technologies. Implement optimization models and
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environmental datasets; • Skills in statistical analysis and data processing; • Experience or interest in ecological modeling and spatio-temporal analyses; • Proficiency in scientific programming tools (R, Python