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8 Nov 2025 Job Information Organisation/Company CNRS Department Laboratoire d'analyse et d'architecture des systèmes Research Field Computer science Mathematics » Algorithms Researcher Profile First
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to develop Computer Vision algorithm and Interfaces for collaborations with Historians teams. Specifically, the research will focus on structured data with clear repeated patterns, such as characters in a text
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between the simulator and sensors located in the operating room. This will require implementing data acquisition algorithms and data processing algorithms within the simulator. The second point concerns
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database
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application will concern multi-sensors based systems for autonomous vehicles embedding AI perception or decision modules. The experiments will rely on embedded platforms available at LCIS, including
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properties and great promise for various applications, including energy storage, supercapacitors, gas sensors and photovoltaics. The goal of the PhD program is to perform systematic experimental and
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» Algorithms Researcher Profile First Stage Researcher (R1) Country France Application Deadline 27 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1
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at the University of Manchester (# 6) and 30th November 2025 for all other projects. Applicants are advised to familiarise themselves thoroughly with the projects for which they apply and be ready to
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, the doctoral candidate will experience a 3-week placement at the University of Manchester (UK) in the lab of Clair Baldock for cryo-electron microscopy training and a 3-month placement at Mimetas (Leiden
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
of operating with a very limited number of sensors. The core concept relies on signature-informed modeling. The principle is to extract only essential and robust information about key parameters of interest from