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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 4 days ago
personalized learning pathways to students. These systems have demonstrated effectiveness across tens of thousands of classrooms in France (primary, middle, and high schools, across multiple disciplines
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to this exciting endeavour. You will work on LCA and waste heat optimisation problems in European and national projects, being responsible for the LCA calculations and optimisation methods & algorithm. You will
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environmental sensing. The incumbent will contribute to the development and deployment of real-time correction algorithms and hardware systems, leveraging GLAO technologies in collaboration with ULTIMATE-Subaru
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environment such as Matlab/Simulink. Design and develop an intelligent energy management system for the e-vessel microgrid for coordinated control of multiple energy sources considering cost, carbon, and
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of HLA donor search algorithms and other non-HLA factors, such as ABO, age and CMV status. Analyze donor searches and apply current donor selection algorithms and strategies. Responsible
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across disciplines. The candidate will be attached to both FORM (hosted by the Department of Mathematics and Computer Science) and DIAS and should be prepared to engage in multiple and diverse research
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that reduce raw data at the sensor level. You will develop AI and machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability
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from multiple systems and sources to answer key operational questions. Deep experience using a variety of data mining/data analysis methods to build and implement dashboards, models and algorithms
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of AI for the integration of multimodal healthcare data specifically incorporating patient preferences. This includes investigating new methods but also designing and benchmarking integration algorithms
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop