PhD Training Project
The Teaching Board approves the training project for each PhD student.
The PhD student’s training project consists of:
- developing an individual research program referring to a specific disciplinary field among those on which the Course is focused, under the guidance of the Supervisor;
- attending teaching activities at doctoral level complementary to the research not lower than 20 ECTS. The recognition of the ECTS, which can be acquired by attending courses and other educational activities, is carried out by the Teaching Board which authorizes the attendance and assesses the results. Educational activities, which can also be organized in common between more than one PhD course, also include training activities aimed at supporting research activities and providing tools to outline the professional identity of future research doctors.
The PhD course in Computer Science and Artificial Intelligence sets out the acquisition of ECTS for
- attendance (and passing possible final tests) of specialist courses chosen among those scheduled annually by the Teaching Board
- attendance (and passing possible final tests) of specialist courses organized by other universities / research institutes / companies. The Teaching Board assesses the suitability of these activities with respect to the student’s training and research objectives and establishes the number of credits to be awarded.
The Teaching Board annually evaluates the training and research activities carried out by each PhD student for the admission in the following year and to the thesis assessment stage.
The individual research program ends with the writing of the thesis. The thesis, written in English, must contribute to the advancement of knowledge or methodologies in the chosen field of investigation.
Courses
2022-2023
- AI for 3D digital heritage. Board contact: Fabio Remondino (FBK)
- Complex systems and artificial life. Board contact: Luca Di Gaspero
- Deep Learning for Computer Vision. Board contact: Christian Micheloni
- EQAI 2023 – Quantum Machine and Deep Learning. Board contact: Giuseppe Serra
- Ontology Engineering in practice. Board contact: Vincenzo Della Mea
- Optimization and Machine Learning. Board contact: Luca Di Gaspero
- Reactive Synthesis: Main Achievements and Current Trend (INF/01), board contact: Angelo Montanari, Gabriele Puppis
- Statistical models and statistical learning methods for the analysis of complex data. Board contact: Alberto Policriti
Graduate students can also attend selected courses of the master programs in Computer Science, Mathematics, and Multimedia, Communication and Information Technology.
In addition, the courses of the Scuola Superiore (School for Advanced Studies of the University of Udine) are open to PhD students.
2021-2022
Basic courses (4+2 CFU or 6+2 CFU):
- Algorithms and Data Structures for Massive Data (INF/01), board contact: Alberto Policriti
- Artificial Intelligence and Machine Learning from the Data (INF/01), board contact: Niki Martinel
- Quantum Computing and Communication (INF/01), board contact: Federico Fogolari, Carla Piazza
- Web Information Retrieval (ING-INF/05), board contact: Stefano Mizzaro
Advanced courses:
- Evaluation Metrics for Artificial Intelligence (ING-INF/05), board contact: Stefano Mizzaro
- Manage Your Requirements Using AI (INF/01), board contact: Angelo Susi
- Quantum Computing for Artificial Intelligence (INF/01), board contact: Carla Piazza
- Reactive Synthesis: Main Achievements and Current Trend (INF/01), board contact: Angelo Montanari, Gabriele Puppis
Graduate students can also attend selected courses of the master programs in Computer Science, Mathematics, and Multimedia, Communication and Information Technology.
In addition, the courses of the Scuola Superiore (School for Advanced Studies of the University of Udine) are open to PhD students.
