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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | about 2 months ago
benefit from the hardware and software resources of the lab experimental platform. The student will be supervisedthroughoutthePhD by Margot Vulliez and David Daney, researchers specializing in modeling and
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(and firmware development) with lab work alongside our microfluidics team. You also take part in the design, build, testing and iteration of fluidic instruments and platforms, integrating sensors, pumps
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of feedstocks, catalysts, and products using advanced analytical techniques (TGA/DSC, BET, elemental analysis, GC-MS, XRD, SEM-EDS), combined with the design and operation of a dedicated validation test bench
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
phenomena. New meta-model architectures based on learning may be proposed and tested on complex EDF use cases. However, this is not sufficient: can such a surrogate, learned from simulation data, predict
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In 2024, nearly 300,000 anti-doping tests were conducted under the global regulatory system established by the World Anti-Doping Agency (WADA) since 1999. WADA's mission is to harmonize rules and
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number of testing participants will be conducted accordingly. For both cases, evaluation criteria will be defined, and metrics will be extracted using objective and/or perceptual tests. - Getting familiar
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facilitating open science implementation by improving on current tools and developing newer and more adapted ones. To solve the hypothesis, EIOSE considers three inter-related angles. The first angle will test
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the performance of the system. This will include (1) the ease of learning new gestures with Mapping-by-Demonstration ; (2) the quality of control after learning. Experiments involving a number of testing
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to align neuromorphic algorithms with the physical constraints of the target hardware. This hardware–software co design effort will involve: • Deepening and extending NSS-related machine learning and
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authorities. Indicators: 1. Number and quality of scientific output (publications, software, patents, books) 2. Establishment of institutional partnerships formalized by contracts 3. Influence within