Program Overview
In their application, students must indicate the preferred track(s) of specialization:
Upon admission, students enrolled in the Ph.D program in Systems Science prepare a tailored study plan together with the Ph.D. supervisor to meet the specific requirements of the planned research activities, selecting subjects from the following list:
List of Ph.D. courses
Advanced Methods for Complex Systems
Advanced Topics in Machine Learning
Advanced Topics in Computational Mechanics
Applications of Stochastic Processes
Basics of Technological, Social, and Legal Aspects of Cybersecurity
Chaotic Systems
Coarse-Graining in Complex Systems
Complexity in Ecology
Computational Contact and Fracture Mechanics
Computational Fluid Dynamics for Incompressible Fluids
Concurrent Programming
Dynamics on Complex Networks
Elements of Statistical Physics and Statistical Inference
Foundations of Probability and Statistical Inference
Fundamentals of Academic Entrepreneurship
Fundamentals of Numerical Analysis
Funding Opportunities and Management of Intellectual Property
Game Theory
Higher-Order Interactions
Introduction to Life Cycle Assessment
Introduction to Machine Learning
Introduction to Network Science
Markov Processes
Maximum-Entropy Models of Complex Systems
Model Predictive Control
Network Neuroscience and Medicine
Network Reconstruction
Numerical Methods for Optimal Control
Numerical Methods for the Solution of Partial Differential Equations
Numerical Optimization
Optimal Control and Differential Games
Publication Strategies and Scientific Dissemination
Python for Data Science
Principles of Digital Twins
Principles of Programming with Python
Probabilistic Models of Cognition
Reduced Order Models and Applications
Reinforcement Learning
Research Topics in Software Quality
Software Verification
Software Security
Vulnerability Assessment and Penetration Testing
Upon completion of the first year, Ph.D. students are expected to have passed the selected courses and dedicate themselves full-time to their research.