Program
The Ph.D. program is structured as a three-year educational and research journey, requiring the acquisition of 180 ECTS (European Credit Transfer and Accumulation System) credits. These are distributed across coursework, scientific participation, international mobility, research activities, publications, thesis work, and other activities.
The faculty board assigns each Ph.D. student a mix of courses (usually 6 courses for no less than 15 ECTS) to enhance their knowledge. The courses are selected based on the student’s interests and research topic and must be completed within the three years (typically in the first two years, allowing the third year to focus on the thesis). Courses are primarily offered at UNIGE but can also be taken at various universities or research centers hosting the doctoral candidates throughout their training program. These courses are accessible both in-person and remotely via web technologies.
Ph.D. candidates will focus on developing a combined use of modeling, simulation, and optimization techniques, algorithms, data analytics, artificial intelligence, software environments, and methodologies and techniques for social research closely related to big data. These tools are used to support decision-makers in a wide range of applications, mainly in the fields of logistics and transportation, industry and industrial plants, energy, green transition, security, autonomous vehicles, humanitarian aid management, and public administration.
To progress to each subsequent year of the Ph.D. program and to be admitted to the thesis defense, each Ph.D. student’s progress will be evaluated based on their attendance at courses, doctoral schools, seminars, and workshops; participation in scientific conferences; presentations of research work; publications; periods of international mobility; and overall research activity. More specifically, admission to the thesis defense requires meeting the following criteria: publication of at least two (2) research outputs uploaded to IRIS.
The faculty board will assign each Ph.D. student one or two supervisors from the faculty board, potentially including an external tutor. A supervisor may be from a foreign university. Unless otherwise specified in the call for applications, students must conduct a research period abroad under the supervision of a foreign tutor for at least six months.
The faculty board guides Ph.D. candidates through their three-year program and is responsible for reviewing and approving the research presentations and summaries that each Ph.D. student must present annually. The final defense of the thesis will be based on the acceptance of the candidate’s final thesis by the faculty board and the presentation of their work before the board. The degree will specifically reference the international Ph.D. in Planning and Decision Methods. If conditions allow, the student may earn a double degree: the first in Planning and Decision Methods and the second in a related Ph.D. program at the supervisor’s foreign university.
Training Program and Ph.D. Courses
The training program requires participation in, and successful completion of exams for, at least 6 elective courses (totaling a minimum of 15 ECTS) from the core courses offered within the Ph.D. program, which are related to the aforementioned themes and listed in the program.
Additionally, students can choose other courses from those offered by the University, which are regularly updated on the UNIGE website, or from other doctoral programs or foreign institutions. These courses are aimed at deepening the understanding of relevant themes and methodologies, particularly on an interdisciplinary level.
The program includes participation in workshops, seminars, and doctoral schools, as well as the presentation of one's research at international conferences. The publication of articles in international journals is highly encouraged. An integral part of the training program, aimed at achieving high professional qualifications, is the experience gained through internships or close collaboration with companies.
International Mobility
During the three-year program, Ph.D. candidates are required to spend a total of at least 6 months abroad (which may be divided into two 3-month periods) to conduct in-depth research activities in their respective fields, unless otherwise specified in the call for applications.
2024-2025 International PhD in Planning and Decision Methods
University of Genoa
Planned teaching activities.
n. | Teaching title | N. hours | Teaching Professor | Recommended PhD year for attendance | Course description | Schedule | CFU |
1. | Foundations of Modelling and Design of Complex Systems | 12 | A.G. Bruzzone | 1st year | Foundation on Complex Systems. Transfer of knowledge about Simulation Paradigms and Modeling Methodologies effective for addressing Complex Systems. Transfer of capabilities to analyze real problems and case studies corresponding to Complex Systems. Acquisition of skills in Conceptual Modeling applied to Complex Problems. Acquisition of Skills in design of Simulation Architectures and Model Development applied to Complex Systems. | September – October 2024 | 3 CFU |
2. | Foundations of Mathematical Modeling & Continuous / Discrete Simulation | 12 | A. G. Bruzzone | 1st or 2nd year | The course aims to provide a presentation of the most common partial differential equations (PDE) and their solution techniques through an analysis of various applications. The emphasis is devoted to second order PDE and the understanding of the specific techniques for elliptic, parabolic and hyperbolic cases. | March 2025 | 3 CFU |
3. | Computational Intelligence - Machine Learning for Decision Making | 12 | F. Bellotti | 1st or 2nd year | Contents: Neural networks; fuzzy logic systems; evolutionary computing; swarm intelligence; neuro-fuzzy and fuzzy neural systems; hybrid intelligent systems, machine learning; classification, regression learning, clustering. | July 2025 | 3 CFU |
4. | Planning models and spreadsheet optimization | 8 | A. Sciomachen | 1st year | Lessons will cover Linear Programming Problems and network optimization problems, the related basic theory, the structure of the related models. Multi period and multi-level planning problems will be faced. Case studies will be formulated and solved with Excel. Participants will be asked to develop and analyse a case study. | Novembre 2024 | 2 CFU |
5. | Foundations of Programming for Problem Solving in Python | 8 | C. Cerrone | 1st year | PhD Students have to follow the lessons on the fundamental concepts of the Course as well as to discuss a Project Work to be finalized and discussed | Decembre 2024 | 2 CFU |
6. | Strategic Engineering: The Closed Loop M&S, Data Analytics & AI at Work | 8 | A. G. Bruzzone | 2nd year | The Course presents the main Principia of Strategic Engineering as well as examples and case studies related to this innovative discipline based on the closed loop among Data Analytics, AI, M&S and Big Data from multiple real and virtual Sources | March 2025 | 2 CFU |
7. | Discrete Event Simulation Models for Strategic Decisions | 8 | A. Sciomachen | 2nd year | The Course presents how Discrete Event Simulation could be effectively used to support Strategic Decision Making | March 2025 | 2 CFU |
8. | Strategic Planning for Logistics and Transportations | 8 | E. M. Cepolina | 2nd year | Models and Methodologies to address Strategic Planning in Logistics and Transportations | June 2025 | 2 CFU |
9. | International Law for Conflicts and Cyber Security | 8 | S. Dominelli | 2nd year | The Course addresses the International Law as well as up to date advances in this sector with special attention to Crisis, Armed Conflicts and Cyber Security/Defense. The course can be followed on the Microsoft Teams channel with code b709nv8, accessible at the following link. | June 2025 | 2 CFU |
10. | Geomatics & Strategic Decision Makers | 8 | I. Ferrando | 2nd year | The Course provides foundations of Geomatics with special attention to Strategic Decisions | February 2025 | 2 CFU |
11. | Paper writing | 12 | M. Marchese | 1st year | The course aims to provide some basic elements to: choose a research topic; manage and use sources; do a novel, serious, and useful research; describe and explain a research. |
January 2025 | 3 CFU |
12. | Introduction to Big Data: Concepts and Techniques | 8 | C. Cerrone | 1st - 2nd year |
|
| 2 CFU |
13 | Explanable AI |
| T. Cerquitelli | 2nd year | Understanding the inner workings of a model and the reason for its decisions is important for trusting the outcome of a machine-learning process. However, many machine learning models do not reveal their internal logic leading to predictions, so they are called "black box models." The explainability of a model in its many facets contributes to the robustness and reliability of any machine learning application. It supports most phases, from design to deployment of ML applications, from model validation and testing to model debugging and verification. In addition, explaining the results of ML algorithms can help end users understand the reason for a decision and trust the model's outcome. This course will introduce modern explanation methods for ML algorithms. Participants will be asked to perform a hands-on activity on the course topic and write a short report. | July 2025 | 3 CFU |
2023-2024 International PhD in Strategic Engineering and Decision Methods
University of Genoa
Planned teaching activities.
n. | Teaching title | N. hours | Teaching Professor | Recommended PhD year for attendance | Course description | Schedule | Notes |
1. | Foundations of Modelling and Design of Complex Systems | 8 + 4 | A.G. Bruzzone | 1st year | Foundation on Complex Systems. Transfer of knowledge about Simulation Paradigms and Modeling Methodologies effective for addressing Complex Systems. Transfer of capabilities to analyze real problems and case studies corresponding to Complex Systems. Acquisition of skills in Conceptual Modeling applied to Complex Problems. Acquisition of Skills in design of Simulation Architectures and Model Development applied to Complex Systems. | September 25, 2023, 14:00-18:00, October 2, 2023, 14:00-18:00 | Compulsory teaching |
2. | Foundations of Mathematical Modeling & Continuous / Discrete Simulation | 8 + 4 | A. G. Bruzzone | 1st or 2nd year | The course aims to provide a presentation of the most common partial differential equations (PDE) and their solution techniques through an analysis of various applications. The emphasis is devoted to second order PDE and the understanding of the specific techniques for elliptic, parabolic and hyperbolic cases. | March 18, 2024, 14:00-18:00, March 25, 2024, 14:00-18:00 | Compulsory teaching |
3. | Computational Intelligence - Machine Learning for Decision Making | 12 | F. Bellotti | 1st or 2nd year | Contents: Neural networks; fuzzy logic systems; evolutionary computing; swarm intelligence; neuro-fuzzy and fuzzy neural systems; hybrid intelligent systems, machine learning; classification, regression learning, clustering. | July 2024; day to be defined | Compulsory teaching |
4. | Foundations of Linear programming and network optimization with spreadsheet optimization | 8 | A. Sciomachen | 1st year | Lessons will cover Linear Programming Problems and network optimization problems, the related basic theory, the structure of the related models. Multi period and multi-level problems will be faced. Case studies will be formulated and solved with Excel. Participants will be asked to develop and analyse a case study. | November 29, 2023, 10:00-14:00, November 30, 2023, 14:00-18:00 | Compulsory teaching |
5. | Foundations of Programming for Problem Solving in Python | 8 | C. Cerrone | 1st year | PhD Students have to follow the lessons on the fundamental concepts of the Course as well as to discuss a Project Work to be finalized and discussed | December 4, 2023, 09:30-13:30, December 5, 2023, 9:30-13:30 | Compulsory teaching |
6. | Strategic Engineering: The Closed Loop M&S, Data Analytics & AI at Work | 8 | A. G. Bruzzone | 2nd year | The Course presents the main Principia of Strategic Engineering as well as examples and case studies related to this innovative discipline based on the closed loop among Data Analytics, AI, M&S and Big Data from multiple real and virtual Sources |
| Optional teaching |
7. | Discrete Event Simulation Models for Strategic Decisions | 8 | A. Sciomachen | 2nd year | The Course presents how Discrete Event Simulation could be effectively used to support Strategic Decision Making |
| Optional teaching |
8. | Strategic Planning for Logistics and Transportations | 8 | E. M. Cepolina | 2nd year | Models and Methodologies to address Strategic Planning in Logistics and Transportations |
| Optional teaching |
9. | International Law for Conflicts and Cyber Security | 8 | S. Dominelli | 2nd year | The Course addresses the International Law as well as up to date advances in this sector with special attention to Crisis, Armed Conflicts and Cyber Security/Defense. |
| Optional teaching |
10. | Geomatics & Strategic Decision Makers | 8 | I. Ferrando | 2nd year | The Course provides foundations of Geomatics with special attention to Strategic Decisions | 22-26 January 2024 | Optional teaching |
11. | Paper writing | 12 | M. Marchese | 1st year | The course aims to provide some basic elements to: choose a research topic; manage and use sources; do a novel, serious, and useful research; describe and explain a research. |
January 2024, days to be defined | Optional teaching |
12. | Math-heuristics in Python | 8 | C. Cerrone | 2nd year | The Course presents advanced use of Python to implement models and heuristic algorithms to address complex problems |
| Optional teaching |
13 | Explanable AI |
| T. Cerquitelli | 2nd year | Understanding the inner workings of a model and the reason for its decisions is important for trusting the outcome of a machine-learning process. However, many machine learning models do not reveal their internal logic leading to predictions, so they are called "black box models." The explainability of a model in its many facets contributes to the robustness and reliability of any machine learning application. It supports most phases, from design to deployment of ML applications, from model validation and testing to model debugging and verification. In addition, explaining the results of ML algorithms can help end users understand the reason for a decision and trust the model's outcome. This course will introduce modern explanation methods for ML algorithms. Participants will be asked to perform a hands-on activity on the course topic and write a short report. | Early July 2024 | Optional teaching |