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6 Sep 2025 Job Information Organisation/Company CNRS Department UMR 5203 Research Field Biological sciences Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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of the team but speaking French or an interest to learn French will be advantageous. Persons from historically underrepresented groups in academia are especially encouraged to apply. We are hiring a
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available for onsite translation, how the axonal and dendritic RNA landscapes are locally organized and regulated to integrate external local signals is to date unclear. The objective of the PhD project will
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of machine learning algorithms are of real interest in improving the accuracy of water quality measurements, particularly in identifying, accounting for, and neutralizing ionic interference. The second key
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD project will be carried out in the "Meiosis and
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of IsoPROPEL is to develop fuel cells that utilize the isopropanol/acetone couple for boats navigating European waterways (canals and rivers). The recruited candidate will be affiliated as a PhD student at ENS
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: ANR JCJC “NanoG4V” : ANR-24-CE51-7558 Expected Outcomes By the end of the PhD, the candidate is expected to: • Acquire solid expertise in the synthesis and advanced characterization of quantum-grade