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, using the ModLoc approach developed by the group (Nature Photonics, 2021). A key aspect of the project will be the implementation of an original detection strategy based on oblique plane re-imaging
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models, whilst respecting the specific spatial structure induced by river networks. - Cleaning, structuring and analysing historical long-term monitoring data (approx. 200 sites). - Adaptation and
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needed for wet-bulb temperature retrieval -Co-locating satellite data with ground-based HadISD stations -Running neural networks and finding the architecture best suited to a case study on India
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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this limitation by developing a unique platform that combines high-intensity terahertz pulses with ultra-cold environments. This innovative approach will enable researchers to observe and control the motion
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. Opportunities to collaborate with leading experts in molecular computing and DNA-based technologies, in France and Japan. Relevant publications: https://blog.espci.fr/guillaumegines/publications/ The successful
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to the dissemination of ultra-stable frequency and time references over telecommunication networks. Its optical-fiber network and laser stations also provide an experimental platform for the development of new
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the simulation of turbulent flows using a tensor network representation of the Navier–Stokes equations. Unlike recent approaches based on tensor networks, which simulate fluid flows in physical space using finite
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, electron microscopy, X-ray diffraction, etc.). Methodology: • Design and synthesize model substrates to measure the activity of enzyme mimics. • Develop analytical methods to detect and quantify chemical
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on neural networks whose parameters are trained on simulations (SBI — Simulation-Based Inference). The recruited person will work within the ZTF, LSST/DESC and Lazuli collaborations to implement cosmological