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/develop predictive models that can inform future land management and conservation strategies. Responsibilities • Data mining: Compile, review and complete pollen data and age-depth models from existing
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statistical models. Within the Polarity, Division and Morphogenesis team, the candidate will work closely with biologists and physicists to develop approaches integrating spatial transcriptomics, cell dynamics
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(study conducted by our collaborators in Marseille). The postdoctoral researcher will participate in the design, execution, and analysis of behavioral experiments in humans. Furthermore, they will develop
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existing structural and functional MRI data, acquire new data in collaboration with clinical researchers, and prepare publications and conference presentations. - Study preparation - Data acquisition (MRI
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, statistics, quantitative/qualitative analysis. - Appetite for educational AI, LLMs, or data analysis (advanced skills not required but appreciated). Cross-functional skills - Interdisciplinary teamwork
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on the statistical inference of Linear Mixed Models (LMMs). The project's goal is to develop a new breed of Mixed Effects Neural Networks (MENN) for Genome InterpretationI that take the best from both worlds, merging
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ancestral recombination graphs to study of the genetic basis of diseases, incorporating ancient genomes • Apply and develop methods for partitioning heritability and estimating genetic correlations • Develop
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collaborate with ARCHIVES project partners to ensure coordinated progress and sharing of results. · Develop solutions combining numerical modeling, mathematical methods, and statistical/AI approaches
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The objective of this position is to develop a robust and efficient framework
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
and theoretical questions related to statistical modeling, prior design in the Bayesian framework, convex and non convex optimization, stochastic optimization. He/she is expected to develop commented