Internship M2 in Industrial Engineering H/F

CESI •

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Two-level dynamic scheduling for a reconfigurable production system Keywords: RMS, Scheduling, Bi-level optimization, Discrete-event Simulation In an industrial context characterized by growing demand uncertainty, high product variability, and significant cost and deadline constraints, Reconfigurable Manufacturing Systems (RMS) offer a promising alternative to traditional production systems. In addition to their flexibility and resilience in the face of uncertainty, RMS also offer significant potential for addressing sustainable development challenges by promoting better use of resources, reducing energy consumption, and limiting the environmental footprint throughout the production cycle. RMS, characterized by their modularity and reconfigurability, require dynamic resource management to maximize performance while ensuring the flexibility needed for frequent changes. Two-level dynamic scheduling meets this requirement by combining: Strategic planning, which defines the optimal resource configurations over the medium or long term [1, 2], and Operational management, which organizes daily tasks and operations in real time [3, 6, 7]. This dual approach not only ensures efficient resource allocation, but also guarantees an appropriate response to disruptions, whether internal (breakdowns, human error) or external (changes in demand). Thus, dynamic two-level scheduling represents a promising approach to managing the complexity and uncertainty inherent in RMS, combining performance stability (economic, environmental, and societal) with flexibility in the face of frequent changes. The issue concerns the coupling between the strategic and operational levels. This coupling is particularly complex in RMS due to their ability to change variety and production capacity [4]. The two-level approach must therefore take this specificity into account. In order to anticipate configuration changes at the strategic level, it is essential to assess the room for maneuver of a given configuration. In this context, the notion of a “flexibility corridor” emerges as a key concept for characterizing this margin for action [5]. These “flexibility corridors” are still poorly formalized in the literature and therefore deserve to be precisely defined and quantified, taking into account the constraints of reconfiguration, capacity, and profitability. This notion of a “flexibility corridor” is a potential decision-making tool for proactively anticipating and managing reconfigurations. Objectives Two-level modeling: Develop a mathematical model that integrates strategic objectives such as reducing costs (upper level) and operational scheduling (lower level), while taking into account the specific characteristics of RMSs, such as scalability (the ability of a system to adapt its production capacity) and convertibility (the ability of a system to absorb disturbances, such as machine breakdowns or demand fluctuations). Simulate different production scenarios incorporating disruptions in order to evaluate the trade-off between system performance and flexibility. Identify the profitability limits of a given configuration as a function of customer demand while maintaining an acceptable level of performance; define “flexibility corridors” for the considered use case. Expected scientific/technical production A mathematical framework integrating strategic objectives and operational scheduling, accounting for RMS features such as scalability and convertibility. A simulation method evaluating system performance and flexibility under disruptions. A decision tool for sizing flexibility corridors. A scientific report or publication draft, presenting the methodology, results, and implications for large-scale mobility optimization. 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. 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. These two teams develop and cross their research in application areas such as Industry 5.0 Construction 4.0 and Sustainable City Digital Services Areas supported by research platforms, mainly in Rouen dedicated to Factory 5.0 and in Nanterre dedicated to Factory 5.0 and Construction 4.0. Links to the research axes of the research team involved CESI Lineact Research Thematic: Decision Support for Production Systems Bibliography [1] Ashraf, M., Hasan, F.: Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology 98(5), 2137–2156 (2018) [2] Haddou Benderbal, H., Dahane, M., Benyoucef, L.: Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (rms) design under unavailability constraints. International Journal of Production Research 55(20), 6033–6051 (2017) [3] Ning, T., Huang, M., Liang, X., Jin, H.: A novel dynamic scheduling strategy for solving flexible job-shop problems. Journal of Ambient Intelligence and Humanized Computing 7(5), 721–729 (2016) [4] Yelles-Chaouche, A.R., Gurevsky, E., Brahimi, N., Dolgui, A.: Reconfigurable manufacturing systems from an optimisation perspective: a focused review of literature. International Journal of Production Research 59(21), 6400–6418 (2021) [5] Azab, A., ElMaraghy, H., Nyhuis, P., Pachow-Frauenhofer, J., Schmidt, M.: Mechanics of change: A framework to reconfigure manufacturing systems. CIRP Journal of Manufacturing Science and Technology 6(2), 110–119 (2013) [6] Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Multi-objective sustainable flexible job shop scheduling problem: Balancing economic, ecological, and social criteria. Computers & Industrial Engineering, 110419 (2024) https://doi.org/10.1016/j.cie.2024.110419 [7] Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Dynamic and sustainable flexible job shop scheduling problem under worker unavailability risk. In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), pp. 1126–1131 (2024). 10711610 Requirements The candidate should be a Master’s student (M2) or in the final year of an engineering school program, with a background in industrial engineering, operations research and optimization. They should have knowledge and experience in several of the following topics: Mathematical modeling Optimization / metaheuristic algorithms Simulation Programming skills Good written and oral communication skills in English Additional knowledge in discrete-event simulation and tools (e.g., FlexSim) would be an advantage. Applicants are required to submit the following documents as part of their application: A curriculum vitae (CV) detailing academic background, technical skills, and relevant experiences A motivation letter (maximum one page) expressing interest in the internship and alignment with the research topic Academic transcripts (relevé de notes) from the current and previous years of study Any reference letters or supporting documents that may strengthen the application

AI Engineer

Capgemini Engineering Tunisia •

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About Capgemini Engineering Capgemini Engineering is a global leader in engineering services. We bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think creatively to deliver unique R&D and engineering services across industries. Join Us for a Career Full of Opportunities Where your ideas matter. Where you can make a real difference. Where no two days are the same. Your Role As an AI Engineer, you will contribute to the design, development, and deployment of artificial intelligence solutions across a wide range of use cases. You will be responsible for building and training machine learning models, preparing high-quality datasets, and supporting the integration of AI components into enterprise or embedded environments. You will: Support the full lifecycle of AI projects: data exploration, preprocessing, model training, evaluation, and deployment. Develop ML models using modern frameworks and contribute to feature extraction, optimization, and validation. Work with cross-functional teams (data engineers, architects, business analysts) to translate requirements into robust AI solutions. Participate in the implementation of AI solutions deployed on cloud, on-premise, or edge environments. Contribute to PoCs, experimentation, and continuous improvement of AI components, pipelines, and reusable assets. Your Profile Bachelor’s or Master’s degree in Artificial Intelligence, Data Science, Computer Science, Applied Mathematics, or related fields. Practical experience in applying machine learning techniques to real-world problems. Exposure to computer vision, sensor data processing, or similar applied AI domains is a plus. Experience building and evaluating ML models using mainstream ML frameworks. Curious, analytical, structured, and motivated to grow within an AI-centered technical environment. Comfortable working within Agile teams and collaborating on multi-disciplinary projects. Strong ability to learn new frameworks, libraries, and deployment technologies. Excellent communication in English & French and teamwork abilities. Ability to adapt to evolving technologies and requirements. Mandatory Skills Machine Learning & AI Strong understanding of machine learning principles, evaluation methodologies, and optimization techniques. Experience with Python and common ML frameworks such as PyTorch, TensorFlow, or similar. Ability to preprocess and clean structured or unstructured data, including sensor data or high-dimensional data sources. Familiarity with: Feature engineering techniques Classical ML algorithms and modern deep learning architectures Model validation, testing, and performance tuning Data & Engineering Skills Solid experience with NumPy, Pandas, and related data manipulation libraries. Understanding of how to manage datasets for training and inference (e.g., batching, augmentation, normalization). Exposure to building deployable AI/ML components, including cloud endpoints or lightweight models for edge or resource-constrained environments. Edge / Deployment Basic understanding of deploying ML models outside notebooks: as APIs, microservices, or embedded runtimes. Familiarity with concepts such as: On-device inference Model optimization (quantization, pruning, compression) Lightweight runtimes or portable execution formats Experience with time-series data, biometric-style data, or sensor-derived signals is a plus. Experience with computer vision, image-based processing, or age/attribute estimation algorithms considered an advantage (framed generically). Soft Skills Strong analytical and problem-solving capabilities. Ability to explain complex concepts to both technical and non-technical audiences. Proactive, curious, and willing to explore new AI techniques and technologies. Team-oriented, collaborative, and comfortable working in a fast-paced environment. Ability to manage time and deliver reliably across multiple tasks or project phases. What You'll Love About Working Here Join a multicultural and inclusive team environment. Enjoy a supportive atmosphere promoting work-life balance. Hybrid work (depends on the Project) Your career growth is central to our mission. Our array of career growth programs and diverse professionals are crafted to support you in exploring a world of opportunities. Access valuable training and certifications in cutting-edge technologies. Engage in exciting national and international projects. Referral program with bonuses for talent recommendations. About Capgemini Capgemini is a global business and technology transformation partner, helping organizations accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market-leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.