Problem or Industrial Need
Mechatronics students and engineering teams need to move beyond isolated algorithms and connect data, learning models, software and physical systems in realistic applications.
Engineering or Scientific Solution
A project-based artificial-intelligence and digital-systems practice spanning supervised and unsupervised learning, deep learning, NLP, scientific programming, embedded implementation and full-stack system integration.
Sebastian's Technical Contribution
Designed and taught applied AI workflows, guided multidisciplinary implementation in Python and MATLAB, and connected model development with embedded, robotic and digital engineering scenarios.
Methods and Tools Used
- Supervised classification and regression workflows
- Unsupervised clustering and representation methods
- Neural networks, deep learning and NLP foundations
- Python and MATLAB model development and evaluation
- Embedded inference and sensor-data integration
- Full-stack intelligent-system and real-case implementation
Prototype, Simulation and Experimental Evidence
Applied teaching and project workflows
AI and Digital Systems methods were implemented through multidisciplinary mechatronics education and supervised engineering projects.
Software-to-physical-system integration
Learning workflows were connected to sensors, embedded platforms, robotics, simulations and application interfaces.
Measurable Result or Published Finding
AI integrated into mechatronics practice
Students developed complete workflows from engineering data and model selection through validation and system-level implementation.
Diagrams and Publications
Engineering data moves through scientific computation, learning algorithms, validation and physical or digital deployment.
Role, Team Attribution, Institution and Project Context
Research Coordinator and Lecturer of Mechatronics Engineering; curriculum, technical teaching and multidisciplinary project supervision at UIDE.