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Field
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collaborate closely with experimental partners (ICCF, IJL, IC2MP, and Syensqo) to validate computational predictions, ensuring the development of catalysts that are both highly active and stable under harsh
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 9 days ago
Research Framework Programme? Horizon 2020 Reference Number 2026-09952 Is the Job related to staff position within a Research Infrastructure? No Offer Description The research focuses on the development
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and to characterize interactions with fire regimes. Ultimately, these results will also contribute to a combined paleo-modeling approach at the core of the RETROPEST project (https://anr.fr/Projet-ANR
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between contaminants, • the potential role of PFAS and antibiotics in the selection of antibiotic resistance genes (ARGs). The successful candidate will contribute to the development of an integrated urban
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Transitions of Self-Organized Embryonic Systems Gastrulation is a crucial stage of embryonic development, during which an initially homogeneous tissue organizes into distinct cellular layers. Our team recently
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in nematodes. Much of our work now focuses on the evolution of egg-laying behaviour and the transitions to viviparity. In parallel, we are also interested in characterizing the natural history, ecology
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belowground; - development of SDM modeling structures that best reflect plant use of multiple resources (e.g., interactive vs. substitutable resources); - testing the application of resource colimitation theory
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in physics analysis and detector development has also been directed to physics studies for the FCC-ee project and FCC detector R&D, especially in the development of the future ALLEGRO liquefied noble
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applications in personalized medicine. - main mission: The successful candidate will join a multidisciplinary team to explore the biophysical mechanisms of protein aggregation and contribute to the development
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. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained