First Year

Module 1: Methods for Research

The course aims to provide foundational research competencies and soft skills in the following areas:

    • Critical reading and research design;
    • Intellectual property management;
    • Research ethics;
    • Scientific writing, positioning and oral presentation / Academic writing for Engineering;
    • Fundraising, project writing and project management;
    • Use of IT tools for researchers.

Module 2: Research Applied Methods

The course aims to provide multidisciplinary and interdisciplinary knowledge, together with a solid methodological foundation:

    • Design of Experiments (DOE);
    • Statistical learning for research;
    • Applied quantitative methods;
    • Product and process sustainability;
    • Simulation methods and tools.

Module 3: Scientific and Computational Foundations of Artificial Intelligence

The course provides knowledge related to:

    • the evolution of AI as a scientific discipline, analysing the principles of symbolic AI and subsequently those of machine learning and deep learning, with particular attention to convolutional networks and transformer architectures for natural language processing;
    • basic computer science concepts for an operational understanding of AI, including digital data representation, computational processing, and the fundamentals of imperative and declarative programming.

Second Year

The second year includes thematic courses that PhD candidates may select according to their research pathway, choosing from the following options:

    • Academic Entrepreneurship;
    • Advanced course in Python for Machine Learning;
    • Analysis and control of complex networks;
    • From a literature review to a conceptual framework: developing research models and hypotheses;
    • Methods and tools for circular design;
    • Methods and tools for automated management of scientific data;
    • Research methodologies for complex systems;
    • Research topics in Manufacturing and Service Operations Management (M&SOM);
    • Influence of new production technologies on material properties;
    • Advanced computational methods for engineering.

PhD students will be able to participate in research seminars periodically organized across the different areas of interest and will regularly present the progress of their dissertation work in dedicated research seminars.

PhD students will also have the opportunity to attend Summer and Winter Schools on topics aligned with the PhD program.