Design and Development of an AI-, Drone-, and Digital-Twin–Enabled Platform for Anomaly Detection and Sustainable Manufacturing Operations

This PhD project focuses on the design of an intelligent platform integrating artificial intelligence, drones, and digital twins to improve the performance, sustainability, and QSE (Quality, Safety, and Environment) compliance of manufacturing processes. The doctoral researcher will develop deep learning, reinforcement learning, and graph-based models for QSE anomaly detection and multi-criteria optimization (cost, carbon footprint, safety, and service level).

The solution will combine a drone-based inspection system with a digital twin of the production line, validated using real industrial data. The expected deliverables include an operational AI system, a drone–simulator demonstrator, and a decision-support dashboard, contributing to a predictive, resilient, and sustainable industry aligned with SDGs 9, 12, and 13.

Research Environment

Candidates are expected to carry out their research full-time within the structures of the Euromed University of Fes.

PhD Student’s Responsibilities

• Conduct a state-of-the-art review of artificial intelligence applied to industry, inspection drones, and digital twins, identifying the most promising approaches for manufacturing performance and sustainability.
• Design, train, and validate AI models (Deep Learning, Reinforcement Learning, and Graph Neural Networks) for anomaly detection and multi-criteria optimization (cost, carbon footprint, safety, and service level).
• Develop and integrate an experimental platform combining intelligent drones, a digital twin, and a decision-support dashboard, tested on real industrial data.
• Establish rigorous evaluation protocols and analyze performance using key indicators (accuracy, false alarm rate, CO₂ per unit produced, and operational reliability).
• Disseminate and valorize the research results through high-impact scientific publications, international conferences, and industrial transfer activities (patents, demonstrators, and partnerships).

Candidate Profile

• Engineering degree or Master’s degree in Industrial/Manufacturing Engineering, Computer Science/AI, Data Science/Applied Mathematics, or a closely related field.
• Desired skills: Python (PyTorch/TensorFlow), modeling and optimization, image/video processing, foundations in reinforcement learning and graph methods, digital twins and simulation (e.g., AnyLogic, FlexSim, MATLAB/Simulink), and/or KNIME.
• Strong interest in interdisciplinary applied research and sustainability.
• Scientific rigor, autonomy, teamwork, and strong writing skills in both French and English.

The application file must include the following documents:

• CV with photo
• Cover letter
• Copy of diplomas
• Copy of academic transcripts
• Copy of CIN/Passport

Submission of the Application File

The application file must be sent to the Doctoral Studies Center (CEDoc) of the Euro-Mediterranean University of Fes by email no later than February 14, 2026 to the following email address:
Euromed-CEDoc@ueuromed.org

For More Information

Administrative Affairs Officer of the CEDoc:
Ms. Boutaina Jai Mansouri – b.jai-mansouri@emadu.ueuromed.org

Director of Research and of the CEDoc:
Prof. Abdelghafour Marfak – a.marfak@euromed.org

Thesis Supervisor

Pr. AMELLAL Asmae – a.amellal@ueuromed.org