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Field
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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ability to bring these tools to bear on problems in data analysis and complex systems. Experience designing and implementing mathematical measures of complex systems, ideally on graph- or simplicial-complex
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synchrotron X-ray scattering data, including X-ray diffraction and total scattering measurements. The research will emphasize extracting physically meaningful descriptors from complex scattering datasets and
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a relevant field and have strong quantitative and analytical skills, including experience in food systems modelling or closely related areas. Proficiency in data analysis and coding (e.g. Python, R
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mathematical experts at RICAM with the goal to investigate influence of variable root growth coordinate with UZH and RICAM documentation of data, analysis scripts and model code, and organize knowledge transfer
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27 Mar 2026 Job Information Organisation/Company Universite de Montpellier Department Human Resources Research Field Mathematics Researcher Profile Recognised Researcher (R2) Positions Postdoc
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complex contagions on higher-order structures (e.g., hypergraphs and simplicial sets). The work will focus on mathematical modeling with a special focus on network science and complex systems, but a strong
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of molecular complexes directly within cells. A central focus of the project will be the development of improved image simulation frameworks. These simulations will not only serve as benchmarking tools
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). Knowledge of complex network modelling, omics data analysis or computational simulation. Research FieldOther Internal Application form(s) needed Perfil. .pdf English (606.79 KB - PDF) Download Additional