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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by
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. These complementary datasets will be integrated using multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and to reveal the interactions linking microbiome
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and machine learning strategies to extract relevant features, cluster responses, and quantify quality-related patterns. The integration of embedded intelligence is essential to enable real-time
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Linguistics is required. • Programming skills are required. • Knowledge of Natural Language Processing and Machine Learning is preferred. • Fluent English required, both oral and written. French is appreciated
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computational fluid dynamics. • Experience in modeling, uncertainty quantification, or statistical methods. • Experience in data science or machine learning is considered an asset. • Experience with high
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
of scientific articles and the PhD thesis Additional activities : Participation and involvement in the ANR project Scientific mediation and communication Where to apply Website https://jobs.inria.fr/public
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protocols using computer-based data collection software, preparing and submitting ethics applications, drafting informed consent documents, and pre-registering studies. - Submit research protocols
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. The following topics are included in these training chapters: • Artificial Intelligence and Machine Learning. • Explainable AI and Trustworthy AI. • Knowledge Representation and Semantic Technologies
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: https://www.list.lu/ How will you contribute? Do you have proven skills in data science, machine learning, as well as in optimisation? We are looking for a highly motivated and talented Engineer to join
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WDSs and soft skills for the researchers and professionals of the future. The following topics are included in these training chapters: • Artificial Intelligence and Machine Learning. • Explainable