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, hydro-acoustics, data science, geodynamics, geophysics, statistics, Bayesian inference ⁃ Experience with statistical analyses and machine learning techniques ⁃ Programming in C / python / Julia
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maximum likelihood and Bayesian inference frameworks. - Data mining in genome databases. - Large-scale phylogeny reconstruction (archaea, bacteria, and eukaryotes). - Implementation of complex sequence
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
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uncertainties (delays, resources, failures) using various methods, including Bayesian approaches. 3. Optimize the workshop configuration, taking into account scenario variability, by relying on the surrogate
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. - Compilation of a curated catalog of archaeal genomes from public data and newly obtained data within the team. - Orthogroup inference, multi-clade pangenome graphs to detect genes with restricted distributions
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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methods for causal inference. Nature Communications, 14(1), Article 1. https://doi.org/10.1038/s41467-023-37194-5 Delalandre, L., Gaüzère, P., Thuiller, W., Cadotte, M., Mouquet, N., Mouillot, D., Munoz, F
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on published protocoles • Develop and validate live-cell imaging tools to track translation dynamics • Perform real-time imaging in regenerating axons • Study the role of specific factors in translation dynamics
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integrated Optics / Photonics or a related discipline. ● A strong track record demonstrating excellence and creativity. ● Experience in design and simulation of integrated photonics components, including
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tomography (SPECT) is an imaging technique that tracks the 3D distribution of a radioactive tracer administered to a patient to monitor certain biological functions. The long acquisition times (10-40 minutes