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-career scientist to develop cutting-edge machine learning approaches for understanding and designing pathogen antigens. This is a unique opportunity to help shape a new research program at the intersection
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the form of composite granules based on catalysts and graphene/SiC. The consortium brings together six French laboratories (LCS, GREYC, UCEIV, IRCER, Pprime, LRGP) with complementary expertise (materials
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with a PhD in microbiology, molecular microbiology, bioengineering, synthetic biology, or a related discipline. The successful candidate should demonstrate: ▪ A strong research track record, evidenced by
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10 Jul 2026 Job Information Organisation/Company Le Mans Université Department ESO Research Field Agricultural sciences Geography Researcher Profile Recognised Researcher (R2) Positions PhD
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
autonomy to work in close collaboration with a hardware engineer and clinical partners are all valued. A PhD in mechanical engineering, with expertise in shape/topology and system-level design optimization
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Macon at the ATESIA site. Internal Application form(s) needed Offre emploi-202607-09.pdf English (53.83 KB - PDF) Download Additional Information Benefits The contract is for a term of 8 months. The start
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PhD Positions Application Deadline 6 Aug 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research
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materials that can be used in other sectors. A liquid-phase foaming process can be used to form fibrous panels that are both porous and cohesive. This simple process involves incorporating air into a fibrous
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· Evaluating the shape memory effect of the developed thermoplastic compounds · Preparing project deliverables and manuscripts for peer-reviewed journals and conferences · Participating in
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networks). • Compare different strategies (topology, tensor order…) to seek for an optimally compressed representation of turbulent velocity fields and of relevant operators in tensor network form. • Perform