Computer Engineer

Entreprise : CESI est une école d'ingénieurs qui fait de la promotion sociale par l'excellence un modèle de réussite. Rejoignez un environnement stimulant où l'esprit d'équipe, la diversité des projets et l'autonomie ne font qu'un. Découvrez une école qui a su développer un modèle unique et se donne les moyens au quotidien de relever les grands défis de l'époque. Nos 25 campus, 28 000 étudiants, 8000 entreprises partenaires et 106 000 alumni témoignent de l'impact de CESI au niveau national. CESI accompagne ses étudiants en utilisant des méthodes innovantes de pédagogie active. L'établissement forme avec rigueur les futurs ingénieurs, techniciens et managers, dans les secteurs suivants : l'Industrie & l'Innovation, le BTP, l'Informatique et le Numérique et le Développement Durable. Parallèlement, CESI concrétise son engagement dans la Recherche à travers des activités menées au sein de son Laboratoire d'Innovation Numérique, CESI LINEACT. Les partenariats établis avec 130 universités à travers le globe, attestent de l'engagement international de CESI. Ces liens privilégiés offrent aux élèves ingénieurs une mobilité sortante et entrante à l'échelle internationale, façonnée notamment par des stages obligatoires faisant partie intégrante de leur cursus. Description du poste : FUTURE - Framework for Uncertainty-aware and Trustworthy Unified Reasoning in Enabled CPS Abstract Cyber-Physical Systems (CPS) are increasingly deployed in safety-critical settings such as manufacturing, robotics, autonomous infrastructures, and intelligent buildings. Smart Building systems, in particular, rely on dense networks of heterogeneous sensors to monitor energy consumption, indoor environmental quality, and occupant comfort. These systems must operate under heterogeneous data sources, incomplete information, sensor unreliability, and unpredictable disturbances. Traditionally, AI-based decision systems provide deterministic outputs, often without expressing confidence levels or handling conflicting evidence. FUTURE project aims to develop an uncertainty-aware and trust-oriented decision-making framework combining uncertainty modelling, AI-based predictions, and Dempster-Shafer theory. By integrating belief-function reasoning with multi-source evidence fusion, FUTURE enhances the resilience of CPS decision pipelines. A realistic CPS scenario specifically, a Smart Building environment will serve as the experimental validation context, leveraging sensor data to evaluate uncertainty, reliability, and evidence-based decision performance. Keywords : Artificial Intelligence, Belief Functions Theory, Cyber-Physical Systems, Smart building, Data Fusion, Heterogeneous Sources. Scientific Fields : Artificial Intelligence, Trustworthy AI, Uncertainty Modelling, System Engineering, Information Fusion. Work Program / Objectives : The work program of FUTURE is structured around eight major tasks, each corresponding to a key component of the proposed uncertainty-aware decision-making framework. These tasks form a coherent pipeline, from data importation to experimental validation, and are described below. Data Importation – Identify a publicly accessible and representative dataset that reflects real-world smart‑building sensor deployments, enabling evaluation of uncertainty-aware learning, reliability assessment, and evidence fusion in realistic operating conditions. For the FUTURE project, the selected test dataset is the CU-BEMS Smart Building Energy and IAQ Data 1. Data Cleaning, Normalisation, and Harmonisation – Preprocess raw data through noise filtering, unit conversion, timestamp alignment, and signal normalisation. Uncertainty Modeling of Data – Estimate the data uncertainty of multi‑heterogeneous sources. Source‑Aware Categorisation of Data – Organise processed data into meaningful categories. Development of an Uncertainty-Aware AI Model – Implement and train an uncertain Neural Network or evidential deep learning model capable of producing both predictions and associated uncertainty estimates. Transformation of Model Outputs into Belief Masses – Convert AI model outputs into class‑specific supports and residual ignorance caused by uncertainty. Evidence Fusion – Combine belief masses using different composition strategies depending on the level of conflict detected. Decision-Making under Uncertainty – Evaluate how uncertainty and conflicting evidence influence the robustness of the final decision. https://www.kaggle.com/datasets/claytonmiller/cubems-smart-building-energy-and-iaq-data Expected Scientific / Technical Output : Scientific Contributions A unified uncertainty-aware decision framework for CPS. A novel integration of belief‑function theory. Experimental analysis of DS-based fusion in real CPS environments, particularly in Smart Building systems. A reproducible methodology supporting trustworthy AI research. A scientific research report. Technical Contributions A fully implemented Python prototype of FUTURE. A validated CPS case study. Impact FUTURE strengthens resilience in CPS, reduces decision errors under uncertainty, and provides a reference framework for integrating uncertainty‑aware AI in industrial settings. Lab presentation : CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A human‑centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross‑cutting research; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions. Its research is organized according to two interdisciplinary scientific teams and several application areas: Teams Team 1 'Learning and Innovating' mainly concerns Cognitive Sciences, Social Sciences and Management Sciences, Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems...) on learning, creativity and innovation processes. Team 2 'Engineering and Digital Tools' mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of human‑system interactions in particular through digital twins coupled with virtual or augmented environments. Application Areas Industry 5.0 Construction 4.0 and Sustainable City Digital Services Areas supported by research… #J-18808-Ljbffr