Transversal and methodological courses are designed to provide the shared scientific “backbone” of the PhD Programme in Industrial Engineering. While the five curricula focus on vertical technologies, these courses offer the essential mathematical, statistical, and managerial tools applicable across all engineering disciplines. The goal is to equip researchers with the rigorous methodological foundation needed to model complex systems, analyze experimental data, and transform scientific results into innovation, in line with the program’s objectives of fostering entrepreneurship and interdisciplinary research.
The didactic offer covers two main pillars: Quantitative Methods and Innovation Management.
- Statistical Analysis: Statistics for Engineers. Focuses on the rigorous treatment of experimental data, hypothesis testing, and uncertainty quantification, essential for validating scientific research.
- Advanced Optimization: Stochastic and Gradient Methods for Single- and Multi-Objective Optimization. Provides advanced algorithms to solve complex design problems, minimizing costs or maximizing efficiency under conflicting constraints.
- Computational Efficiency: Introduction to Model Order Reduction. Techniques to reduce the complexity of mathematical models, enabling faster simulations without compromising accuracy—crucial for digital twins and real-time control.
- Entrepreneurship: Introduction to Open Innovation, Lean Startup, and Design Thinking. A strategic module aimed at developing an entrepreneurial mindset, teaching how to validate business ideas and manage the transition from lab research to market.
