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English, and the ability to work both independently and collaboratively are expected. Specific Requirements Candidates must have defended their PhD before the start date of the position (January 2027). A
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with a high pulse power device. Another study intends to develop non-thermal electrons distribution which is of trendemous importance for UHI experiments. Durée du contrat (en mois) 2 ans + 2 x 1an
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knowledge of physics/optics (a PhD in these fields is required). Expertise in photonics is a plus. Specific Requirements Knowledge on optical fibers, a taste for precision experimentation, knowledge
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articles, reports, and project proposals. 3) Supervision: Ability to guide and train collaborators (engineers, PhD students). 4) Critical analysis: Interpretation of results and proposal of innovative models
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used by a PhD student, likely starting October 2027, to initiate development of a deep-learning architecture. The refined batch of synthetic data will be used by the PhD student to finalise the deep
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Contribute to the supervision of students where appropriate Experience required : A recent PhD graduate who defended their thesis less than a year ago, with in-depth experience in mass spectrometry-based
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criteria The candidate should hold a PhD in plasma physics and chemistry, materials science, surface science, polymer chemistry, or a closely related scientific field. Experience in low-pressure plasma
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valorization and dissemination of the results.The postdoctoral researcher will also have the opportunity to propose and supervise internships and to contribute to the supervision of PhD students and early-career
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a powerful and compact representation of the replication program, capturing the key regulatory role of epigenetic and genomic context (Arbona, PLoS Comput. Biol. 2023). However, the application
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 22 hours ago
million commits. Most existing tools still treat code at the level of individual text files, which limits their ability to scale when changes span many files (e.g., automated refactorings, dependency