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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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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also
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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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model characterisation strategies definition of the necessary equipment definition of the necessary processing algorithms Implementation/validation/drafting of scientific articles in collaboration with
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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