PhD courses (to fulfill the 12 mandatory CFUs)

Each student may choose his/her courses among those present in the full University Catalog. The courses organized by the Phd in Physics, Astrophysics and Applied Physics are listed below. The exact schedule of the lectures is not yet fixed and will be available after the preliminary meeting of each course.

ASTROPHYSICS AREA

- Advanced topics in astrophysics and plasma physics-Collective phenomena in plasma physics (2 CFU-10 hours)
- Advanced topics in astrophysics and plasma physics-Fundamentals of computational fluid dynamics in astrophysics (2 CFU-10 hours)
- Advanced topics in astrophysics and plasma physics-Cosmology (2 CFU-10 hours)
- Advanced topics in astrophysics and plasma physics-Observations of the CMB (2 CFU-10 hours)
- Advanced topics in astrophysics and plasma physics-Observations and theory of large-scale structure formation (2 CFU-10 hour)
- Advanced topics in astrophysics and plasma physics-Bayesian statistics in astronomy (2 CFU-10 hours)

NUCLEAR AND SUBNUCLEAR AREA

- Advanced topics in particle physics (4 CFU-20 hours)
- Neutrino physics (2 CFU-10 hours)
- Nuclear structure theory: Density functional methods in nuclear physics (2 CFU-10 hours)
- Nuclear structure studied with stable and radioactive beams (2 CFU-10 hours)
- Nuclear structure and reaction dynamics with radioactive beams (4 CFU-24 hours) – This course takes place in Padua

MATTER PHYSICS AREA

- Quantum coherent phenomena (6 CFU-30 hours)
- Quantum theory of matter (6 CFU-30 hours)

THEORETICAL PHYSICS AREA

- Computational, simulation and machine learning methods in high energy physics and beyond: Automated computational tools (3 CFU-15 hours)
- Computational, simulation and machine learning methods in high energy physics and beyond: Monte Carlo Methods (3 CFU-15 hours)
- 4D and 3D Theories with Four Supercharges: Field Theory, D-Branes, Holography and Localization (7 CFU-35 hours)

APPLIED PHYSICS AREA

- Experimental methods for the investigation of systems at the nanoscale (6 CFU-30 hours)

PhD seminars (out 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)
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.