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being reachable within an hour train ride). Website: https://www.iemn.fr/la-recherche/les-groupes/physique/nanostructures-qu… In the digital age, the energy consumption of microelectronic devices presents
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to develop a new experimental method to measure these extremely small surface viscosities. The idea is simple but original: we will perform the measurement at the surface of an horizontal soap film (see figure
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massive collaborative effort among four research laboratories (CETHIL, Lafset, LMFA and LUSAC) to fully develop a versatile reversible sCO2 Brayton cycle targeted to harvest industrial waste heat. In
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the MPN team at LAAS-CNRS, which offers a fabrication platform (cleanroom) and an electrical characterization platform (GHz VNA access), along with associated engineering support. Development
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renewable energy in a dense and usable form. However, the thermodynamic and kinetic barriers associated with CO₂ reduction require the development of high-performance and selective catalysts. The selected
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, immunology, and allergology to better understand the interaction between airway epithelium and mast cells within healthy and diseased airways, identify biomarkers, and develop algorithms for the diagnosis and
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validated algorithms. The objective is to identify sensorimotor signatures of fall risk that may improve current predictive models and contribute to the development of more targeted prevention strategies
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Cantini, works at the interface of machine learning and biology (tools developed by the team: https://github.com/cantinilab ). The team is composed of 9 people : 4PhD students, 3 post doc, 1 research
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 7 days ago
(Loreley, CEDAR and MAGELLAN). This project aims to design and develop an open-source management solution for a federated and distributed data exchange platform (DXP), operating in an open, scalable, and
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://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed by the team: https