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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Job offer - **Company:** UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE - **Location:** Compiègne, France - **Salary:** €31,080.0 - €60,000.0 - **Contract:** Temporary to permanent - **Skills:** Computer Animation, Artificial Intelligence, Computer Vision, Decision Support Systems, Human-Computer Interaction, Machine Learning, Information Technology, Data Analytics, Machine Learning Operations - **Published:** July 29, 2026 - **Apply:** https://utc.recruitee.com/o/enseignant-chercheur-contractuel-fh-ia-pour-lindustrie ## About the Role Research Field Computer science Education Level PhD or equivalent Skills/Qualifications Profile and keywords Profile: Research experience in one or more scientific areas related to the "AI for industry and risk management". Keywords: Artificial intelligence, AI for industry, computer vision, machine learning, trustworthy AI, explainability, uncertainty quantification, robustness, auditability, AI certification. Qualification Required degree: PhD A PhD degree is not required at the time of application, but it will be required following the interview Field: Computer science and related disciplines. The recruited candidate will be expected to: · Work collaboratively and contribute to the scientific coordination and animation of the CAP initiative, · Show interest in technological research and industrial partnerships, · Demonstrate proficiency in English, · Disseminate research results through publications and other forms of valorization ## Description The successful candidate will teach 64 hours per year to strengthen educational activities in the areas of data science, artificial intelligence, and intelligent interactive systems (e.g., machine learning, trustworthy AI, AI-driven decision-making, human-centered AI, data-driven systems, intelligent perception, explainable AI). The recruited faculty member will contribute to existing courses and may develop new ones. Teaching activities will mainly target Master-level programs, in synergy with the new ERASMUS MUNDUS program European Master in Sustainable Systems Engineering (UTC, University of Genoa - Italy, Universitat Politècnica de Catalunya - Spain, and Polytechnic University of Tirana - Albania). The successful candidate will also have the opportunity to contribute to the UTC's computer science engineering programme for students undergoing initial training (FISE) and alternating training with companies (FISA). Research This position is part of the "AI for Industry and Risk Management" CAP (Collaborative Acceleration Program) initiative, from the PostGenAI@Paris hub coordinated by Sorbonne Université. Objectives: The project addresses a central challenge in modern industrial AI: moving from high-performing predictive systems toward trustworthy, auditable AI systems suitable to risk analysis for deployment in critical operational environments. While current AI technologies have demonstrated remarkable performance in areas such as computer vision, predictive maintenance, anomaly detection, quality inspection, forecasting, and decision support, their adoption in industrial contexts remains limited due to several structural limitations: · limited interpretability of model behavior; · weak guarantees regarding robustness and reliability; · insufficient characterization of predictive uncertainty; · lack of robustness to distribution shifts, rare events, and adversarial conditions; · lack of traceability and auditability required by industrial and regulatory standards. The project therefore aims to establish a new methodological framework for Trustworthy Industrial AI, combining statistical rigor, explainability of predictions, uncertainty modeling, compliant with a human-centered supervision. The selected candidate will join the Heudiasyc laboratory and contribute to one or more of the project's scientific axes. The goal is not merely a purely technical vision of prediction tools, but also regulatory compliance and operational trust (industrial acceptance, improved human oversight). · From raw prediction to reliable decision support o estimating confidence levels associated with predictions; o identifying situations outside their operational validity domain; o supporting risk-sensitive industrial decision-making and communicating uncertainty to human operator. · Explainability and interpretability of AI systems o Development of AI models whose behavior can be analyzed, interpreted and audited by domain experts; o explainable machine learning, post-hoc explanation techniques; o attribution methods; o causal and symbolic reasoning approaches; o human-understandable representations of AI decisions. · Quantification and Propagation of uncertainty in industrial environments (noisy sensors, sensor degradation, evolving production processes, rare events, incomplete datasets…) o quantifying epistemic and aleatoric uncertainty; o detecting abnormal or unforeseen situations, domain shift; o calibrating confidence estimates; o propagating uncertainty through AI pipelines. These research directions may be taken using different approaches such as Bayesian learning, evidential learning or conformal prediction to provide AI systems with mechanisms allowing them to "know when they do not know", provide explanations, trigger human intervention when necessary, and reduce unsafe autonomous decisions. The program seeks to improve robustness with respect to several effects, eventually leading towards safety guarantees for machine learning systems, validation protocols and certification frameworks. A long-term ambition is to contribute to future standards and methodologies for the certification of trustworthy industrial AI systems. Research activities may be validated using simulation platforms or experimental systems or data. The recruited candidate will also contribute to the project's partnerships and development strategy. The program is inherently interdisciplinary, combining research in artificial intelligence, machine learning, human-computer interaction and interactive systems. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Focoos AI: Building the Future of Computer Vision](https://www.wearedevelopers.com/videos/1659-focoos-ai-building-the-future-of-computer-vision) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Rethinking Recruiting: What you didn’t know about Responsible AI](https://www.wearedevelopers.com/videos/1090-rethinking-recruiting-what-you-didn-t-know-about-responsible-ai) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [How Data is Shaping our Games](https://www.wearedevelopers.com/videos/176-how-data-is-shaping-our-games) ## Related Articles - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Welcome to Switzerland](https://www.wearedevelopers.com/magazine/4-welcome-to-switzerland) - [Best Companies to Work For in Paris: Top 25 Companies in 2023 ](https://www.wearedevelopers.com/magazine/190-best-companies-to-work-for-in-paris-top-25-companies-in-2023) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [System change: restart as developer?](https://www.wearedevelopers.com/magazine/39-system-change-restart-as-developer)