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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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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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. Our vision and our understanding of supermassive black hole systems have indeed known dramatic progress over the last decade. In particular, they have shown that these objects behave as accelerators
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will involve the production, purification, and characterization of the receptors of interest, in the presence or absence of their effectors (G protein, β-arrestin). The objective is to determine the
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-term reliability. While various contact engineering strategies have been explored, further innovation is needed to enable the widespread adoption of OTFTs in commercial applications. PhD Objectives
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systems, specifically air-sea interactions in the ocean. The primary objective is to develop a consistent and well-calibrated mixed-layer-wave parameterization to capture turbulent kinetic energy (TKE) and
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objective is to develop artificial intelligence methods that complement modeling techniques, particularly at the atomic scale, specific to the physical chemistry of materials. In this context
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on the valorization of lignin-rich co-products, namely Brewers' Spent Grain (BSG) from breweries and lignin extracted from black liquor or wood bark from paper industries. The objective of BioMCat is to develop a novel
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for ensemble measurements as well as single nano-object investigations to uncover the underlying light-harvesting physics. The goal will be to measure the EEE diffusion length via the monitoring of exciton