PHYSICS OF COMPLEX SYSTEMS
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Laurea Magistrale
A Torino
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Descrizione
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Tipologia
Laurea Magistrale
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Luogo
Torino
Motivations
Nowadays, in order to make innovations in an advanced scientific and technological context (like e.g. system biology, neuroscience, computation, network engineering, web economy, financial markets modeling, novel materials), it is mandatory to master the most advanced concepts and methodologies for complex systems. This is why we have set up a Master of Science program, with two parallel educational tracks, which aims to offer the best techniques needed to attack interdisciplinary problems.
Topics
The Master of Science programme is concerned with the study of the following topics:
Modern physics
A solid background on concepts and methods of statistical and quantum solid state physics.
Connections with information theory (both classical and quantum) and biophysics.
An overview on the most advanced techniques in experimental physics and data analysis.
Information technology
State-of-the-art of numerical methods and simulations, both deterministic and stochastic, and their relations with statistical physics.
Main algorithms for combinatorial optimization, statistical inference, machine learning and their applications in bioinformatics.
Engineering applications
Non linear and fluid dynamics problems
Soft matter (liquid crystals, polymers, membranes, gels, granular matter).
Biological systems
Problems in cellular systems biology, in neuroscience and molecular biology analyzed in the framework of computational statistical physics.
Many courses rely on advanced mathematical methods to which specific sections of courses are dedicated.
The fourth semester will offer several short courses on advanced interdisciplinary research topics.
Goals
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Graduates from this master program will be experts in modeling and simulation of complex systems. They can look forward to careers as researchers in development labs or computing centers, senior software engineers, biological physicists, financial analysts and more
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Opinioni
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Ho svolto il mio Erasmus presso quest'università e mi è sembrato ottimo. Ho realizzato il corso in inglese. Ottima scuola con dei professori bravissimi.
← | →
-
Offre un'offerta formativa di alta qualità e ti aiuta ad entrare subito nel mondo lavorativo.
← | →
Valutazione del corso
Lo consiglia
Valutazione del Centro
L. R. T.
Chiara Fogli
Materie
- E-learning
Programma
Insegnamento
Introduction to numerical methods and computational neuroscience
ING-INF/06 (4); FIS/03 (4)
Oppure
Numerical methods and simulation techniques
FIS/03 (8)
Introduction to quantum mechanics, quantum statistics and field theory
FIS/02 (8)
Probability and Information Theory
ING-INF/05 (8)
Advanced experimental physics
FIS/01 (6)
Algorithms for optimization, inference and learning
ING-INF/05 (8)
Condensed matter theory
FIS/03 (10)
Statistical physics and biophysics
FIS/02 (6); FIS/02 (6)
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Statistical physics (A.Pelizzola)
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Biophysics (A.Pelizzola)
Insegnamento
Complex systems: computational aspects and interdisciplinary applications
ING-INF/05 (12)
Non linear physics, dynamical systems and computational science
FIS/02 (6)
Statistical field theory
FIS/02 (6)
Stochastic processes in physics
FIS/02 (6)
Final project work
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Internship ()
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Spring school ()
Insegnamento
Advanced quantum mechanics and quantum statistics
FIS/03 (4); FIS/02 (4)
Fluid Dynamics
ING-IND/06 (8)
Stochastic simulation methods in physics
FIS/02 (3); FIS/03 (3); MAT/06 (2)
Free choice course from Table B
Algorithms for optimization, inference and learning
ING-INF/05 (8)
Condensed matter theory
FIS/03 (10)
Statistical physics and biophysics
FIS/02 (6); FIS/02 (6)
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Statistical physics (A.Pelizzola)
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Biophysics (A.Pelizzola)
Insegnamento
Field theory and critical phenomena
FIS/02 (6)
Free choice course from Table 1
Inference in biological systems
ING-INF/05 (6)
Optimal control and game theory
ING-INF/04 (6)
Out of equilibrium physics
FIS/02 (3); FIS/03 (3)
Final project work
Insegnamento
Finite element modelling
MAT/08 (6)
Materials and characterizations for Micro and Nanotechnologies
FIS/03 (6)
Statistical learning and neural networks
ING-INF/03 (6)
Statistical models
SECS-S/01 (6)
Advanced experimental physics
FIS/01 (6)
Insegnamento
Dynamics over networks
MAT/05 (6)
Finite element modelling
MAT/08 (6)
Optimization for machine learning
ING-INF/04 (6)
Optimization for machine learning
ING-INF/04 (6)
Physiological systems modeling
ING-INF/06 (6)
Statistical models
SECS-S/01 (6)
Insegnamento
Challenge@CLIK - CLEAR - miCro pLastic freE wAteR
ING-IND/13 (2); ING-IND/32 (2); ING-IND/06 (1); ING-IND/22 (1); ING-IND/35 (1); ING-INF/05 (1)
Challenge@CLIK - PeopleFlowing
ICAR/21 (1); ING-IND/17 (1); ING-INF/03 (1); ING-INF/04 (1); ING-INF/05 (1); MAT/09 (1); SPS/08 (1); ICAR/05 (1)
Challenge@CLIK - e-Mobility
ING-IND/10 (2); ING-IND/14 (1); ING-IND/16 (2); ING-IND/31 (2); ING-INF/05 (1)
Challenge@CLIK - performANCE
ICAR/17 (2); ICAR/09 (1); ICAR/10 (1); ICAR/11 (1); ICAR/22 (1); ING-IND/17 (1); ING-INF/05 (1)
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Ti aiuterà a confrontare vari corsi e trovare l'offerta formativa più conveniente.
PHYSICS OF COMPLEX SYSTEMS