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Job description We are recruiting postdocs to contribute to research projects in the Mathematical Modelling of Infectious Diseases Unit, at Institut Pasteur in Paris. The candidates will be expected
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optimization. Familiarity with mathematical modeling tools and solvers (e.g., Pyomo, Gurobi, CPLEX). Knowledge of uncertainty modeling, robust or stochastic optimization is a strong asset. Experience applying
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of glioma extension using magnetic resonance spectroscopy in 7T MRI. Part of this project involves the development of MRI image processing tools, the implementation of mathematical models for the analysis
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ingredients for Earth-like magnetic fields on millennial time scales in dynamo models. The research activities are two-fold. First, the candidate will run numerical dynamo simulations with various combinations
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biology and is resolutely interdisciplinary, integrating approaches in (bio)informatics, (bio)physics, and (bio)mathematics. The “genome mechanics” team is interested in the process of DNA break repair by
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to the comprehensive analysis of field samples and the mathematical modeling of cellular interactions. LPHI includes four teams working on parasite models (Toxoplasma and Plasmodium), thus offering a stimulating working
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learning or applied mathematics. Required skills and qualities: - Fluency with Python programming for data analysis or machine learning, - Knowledge of statistical or probabilistic modelling techniques
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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their metabolic activities to understand their role in carbonate precipitation. The objectives of this work will be to: Map the New Caledonian lagoon and quantify the precipitated carbonate sludge. Model
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analyzing whole organism models (zebrafish and mouse) to dissect the pathophysiology of a recently identified rare pediatric neurometabolic disorder. The approach involves mainly deep longitudinal phenotyping