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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 1 day ago
(videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage According to current regulations: €4.50 per hour Selection
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develop and implement machine learning approaches to analyze these data and extract relevant indicators to improve water resources management. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant
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of the site by managing a variety of tasks. Key Responsibilities Oversee landscaping, cleaning and vending machine frame contracts. Organise lab and office relocations and support the setup of ESRF
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» Computer engineering Researcher Profile First Stage Researcher (R1) Positions Other Positions Application Deadline 20 Oct 2026 - 17:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full
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systems Computer science » Programming Computer science » Computer systems Engineering » Electronic engineering Computer science » Systems design Researcher Profile First Stage Researcher (R1) Recognised
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4 Sep 2026 Job Information Organisation/Company INSA Strasbourg Research Field Engineering » Electrical engineering Engineering » Computer engineering Researcher Profile Recognised Researcher (R2
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cornerstone of Europe’s renewable energy strategy, supplying nearly 30% of renewable electricity and providing essential flexibility for integrating large shares of variable renewable energy sources (vRES
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modern machine learning, with broad implications for both theory and applications. The PhD project focuses on the theoretical foundations and algorithmic design of discrete gener- ative models, situated
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potentials (CCEPs) from electrophysiological recordings obtained during clinical electrical stimulations in patients with epilepsy. Working within a research team at the interface of cognitive/clinical
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(https://endomic.github.io) aims to develop artificial intelligence and statistical-learning methods capable of identifying robust and clinically meaningful disease endotypes from heterogeneous, multimodal