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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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expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and experience
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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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that incorporates a broad range of neutrino and dark-matter models, assessing their effects on large-scale-structure (LSS) statistics as measured by the power spectrum and bispectrum of galaxies or intensity maps
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contract – 2 years Candidates must hold a PhD, in Industrial Ecology, Process Engineering or Mining Engineering, with demonstrated link/application to the mineral raw materials sector. The candidates should
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to carry out the following tasks: Development of analysis scripts for the preprocessing and automated processing of functional neuroimaging data; Statistical modeling of imaging data and evaluation
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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, which combines tools from probability, statistics, PDEs, and extreme-value theory to address climate-related questions. Qualifications: Completed PhD degree in Mathematics, with a solid background in at
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Mathematics » Mathematical analysis Mathematics » Mathematical logic Mathematics » Number theory Mathematics » Probability theory Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Country
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5 Jan 2026 Job Information Organisation/Company Universite de Montpellier Department Human Resources Research Field Physics » Computational physics Physics » Statistical physics Researcher Profile