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Field
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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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research and soft robotics development. PhD project The PhD project will focus on the technical aspects of simulating the physics of the Drosophila larva body. The primary objectives include: Developing a
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of electromagnetic wave physics or astrophysics, considered an asset. - Experience with advanced statistics and Bayesian inference, which will be regarded as a plus. Familiarity with compressed sensing and the ability
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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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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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links) and Space Domain Awareness (tracking satellites, debris, and near-Earth objects). Expanding AO to these domains introduces challenges: extreme performance for faint or high-contrast targets
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Stochastic processes ODE/PDE modeling Control theory This theoretical expertise must be paired with a demonstrated interest and track record in biological applications, particularly in areas such as gene
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investigator and will participate in theoretical and numerical modelling work, as part of a project team that includes doctoral students and technical staff. The main objective of this postdoc position is (1
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experiments, data analysis, field work, and work as part of a project team that includes doctoral students and technical staff. The main objective of this postdoc position is (1) to measure the activity and
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project team that includes doctoral students and technical staff. The main objective of this postdoc position is (1) to develop theoretical modelling approaches (bioenergetics, thermodynamics, ecological