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exciting opportunities for machine learning to address outstanding biological questions. The postdoc to be recruited will be working on the development of machine learning methods for single-cell data. In
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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strategies, and (ii) NIDS evaluators, to enable more informative and reliable evaluation protocols for ML-based IDS/NIDS. Where to apply Website https://institutminestelecom.recruitee.com/o/postdoc-qualite-des
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
specimens. The postdoc will contribute to the development of hybrid modeling and identification approaches that combine classical constitutive frameworks, numerical simulation, and machine learning. The work
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, France [map ] Subject Areas: Probability Statistical Physics Statistics Mathematics Machine Learning / Machine Learning Appl Deadline: 2025/12/21 04:59 AM UnitedKingdomTime (posted 2025/11/25 05:00 AM
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23 Jan 2026 Job Information Organisation/Company IMT Atlantique Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus. LanguagesFRENCHLevelBasic
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed