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, learning, visual literacy, collaboration, shared spaces, physical installations, user experiments. All details here: https://bivwac.fr/jobs/ Where to apply E-mail phd-26_bivwac@inria.fr Requirements Research
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multidisciplinary team led by Valérie Caps (ICPEES/CNRS Chemistry) and Giovanni Manfredi (IPCMS/CNRS Physics), based at the CNRS Cronenbourg campus. Attached to ICPEES, the PhD student will have access to both
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collaboration. The thesis is financed by in2p3. References: I. Stefan et al PLB 779, 456 (2018) https://www.sciencedirect.com/science/article/pii/S0370269318301394?via… C. Portail PhD thesis Sections efficaces de
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sciences First enrollment for a PhD in 2026–2027, in sociology or anthropology Where to apply Website https://recrutement.ehess.fr/828_offre-emploi-doctorante-contractuelle-pour-le-… Requirements Research
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16 Apr 2026 Job Information Organisation/Company Nantes Université Department SUBATECH UMR-CNRS 6457 Research Field Physics Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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20 Mar 2026 Job Information Organisation/Company LEM3 Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Jun 2026 - 00:00
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17 Mar 2026 Job Information Organisation/Company cnrs Department lem3 Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Jun
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 14 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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) Positions PhD Positions Application Deadline 20 May 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU
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) and define the problem mathematically. Secondly, a central component of the PhD will be to learn mappings between heterogeneous spaces through latent-variable models and representation learning. Some