Contacts

Franco Bagnoli franco.bagnoli [at]unifi.it; Francesco Piazza francesco.piazza[at]unifi.it

What is studied

The "Physics of Complex Systems" curriculum has an explicitly interdisciplinary and theoretical-computational approach, focused on acquiring important tools to tackle open problems in various disciplines beyond physics, such as biology, medicine, cognitive sciences, and artificial intelligence. In the spirit of education through research, special attention will be given to selecting, where possible, teaching materials that integrate examples and experimental data from the numerous active laboratories in the Florence area (and beyond) in physics and other disciplines. Students choosing this curriculum are strongly encouraged to get in touch early with research groups at the Department of Physics and Astronomy or other institutions based on their interests, allowing for a gradual approach to research. These activities may take the form of small ad hoc projects, in-depth studies of various course topics, bibliographic reviews, and more. In this regard, it is highly recommended to use elective courses and possible Erasmus periods to deepen the interdisciplinary aspects of the curriculum.

The curriculum in the physics of complex systems provides training that develops skills in analyzing, structuring, and modeling open problems in various fields, helping students build the critical thinking necessary to address complex topics, which is valuable both in fundamental research and applied research.

Courses

If not already selected during the Bachelor's program, the following preparatory course is recommended:

Introduction to the Physics of Complex Systems

Among other courses offered at the University of Florence that can complement an interdisciplinary path, we highlight:

@Mathematics:

Mathematical Modeling for Applications

Mathematical Methods for Applications

LM Artificial Intelligence and LM Data Science @unifi

Statistical Physics and Complex Systems

Data Science for Neurosciences

Fundamentals of Machine Learning

Deep Learning / Deep Learning Applications