PhD courses (to fulfill the 12 mandatory CFUs)

Each student must choose the courses among those listed below. The  lessons schedule will be available after the preliminary meeting of each course. 2018/2019 program lessons.

ASTROPHYSICS AREA (BERTIN)

- Advanced topics in astrophysics and plasma physics (6 CFU-30 hours, M.Bersanelli, C.Grillo, L.Guzzo, M.Romé, G.Lodato, M.Lombardo, D.Maino) LECTURES SCHEDULE

NUCLEAR AND SUBNUCLEAR AREA (ANDREAZZA)

- Nuclear structure and nuclear reactions (6 CFU-36 hours, S.Leoni, G.Colò, F.Scarlassara, A.Vitturi)
- Advanced topics in particle physics (6 CFU-30 hours, A.Andreazza, L.Carminati, C.Pagani, R.Turra, F.Vissani) LECTURES SCHEDULE

MATTER PHYSICS AREA (CASTELLI)

- Quantum theory of matter (6 CFU-30 hours, N.Manini, G.Onida, A.Parola) LECTURES SCHEDULE
- Quantum Coherent Phenomena (6 CFU-30h, M.Genoni, C.Benedetti, F.Castelli LECTURES SCHEDULE

THEORETICAL PHYSICS AREA (FERRERA)

- Introduction to conformal field theory and topological quantum field theory (6 CFU-30 hours, S.Caracciolo, S.Cacciatori)
- An introduction to random matrices (3 CFU-15 hours, L.Molinari) LECTURES SCHEDULE
- Computational, simulation and machine methods in high energy physics and beyond: Automated computational tools (3 CFU-15 hours, F.Maltoni-M.Zaro) LECTURE SCHEDULE
- Computational, simulation and machine methods in high energy physics and beyond: Monte Carlo Methods (3 CFU-15 hours, P.Nason) LECTURES SCHEDULE

APPLIED PHYSICS AREA (VAILATI)

- Experimental methods for the investigation of systems at the nano scale (6 CFU-30 hours, A.Vailati, M.Potenza, P.Piseri, S.Cialdi, C.Lenardi, A.Lascialfari, M.Carpineti, M.Buscaglia, F.Giavazzi, A.Podestà, G.Zanchetta) LECTURE SCHEDULE
- Computing hardware architectures for pattern recognition (3 CFU-15 hours, V.Liberali)

 

PhD seminars (out of mandatory CFUs)

The seminars schedule will be attached when available.

- Computational, simulation and machine methods in high energy physics and beyond: Machine Learning (3 CFU-15 hours, S.Carrazza): An introduction to machine learning techniques including model representation, parameter learning, non-linear models, hyperparameter tune, and an overview of modern deep learning strategies. The seminars will cover the theoretical and mathematical aspects of machine learning followed by practical examples of code implementation using public frameworks. LECTURES SCHEDULE