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Prof. Marc-Emmanuel Dumas in Lille, France. The successful applicant will develop high-throughput high-resolution mass-spectrometry based metabolomic workflows (automatization of sample preparation
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intelligence algorithms, capable of warning far- mers in order to enable early and appropriate interventions. The proposed solution relies on the use of several complementary technologies : • Cameras
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approach based on Deep Learning algorithms will be developed and implemented to obtain additional information by coupling the recorded data. Furthermore, the increase in acquisition rates of measurement
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structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption
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energy recovery linac (ERL) demonstrator at IJCLab, Orsay. ERLs offer a promising way toward the development of future colliders, particularly by providing excellent beam quality while drastically reducing
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to the construction of the detector at the LNGS. Data taking should start in 2029. The subject of this postdoctoral position, funded by the CNRS for two years, is to prepare the analysis of the first data in order to
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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networks are not well-suited to the computational constraints of FHE. The project aims to develop more efficient neural network architectures tailored for encrypted computations. The postdoctoral researcher
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atmospheric sciences • Knowledge of cloud or aerosol physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a
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, funded by the ANR P2S2 project. The position is available initially for a fixed-term duration of 2 years, with the possibility of extension for 1 further year. The P2S2 project aims at developing parton