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

  • at least 10 ECTS
    • with attendance (and passing of possible final test) of courses in the area and discipline of reference, chosen from those scheduled annually by the Teaching Board of the doctoral course. The doctoral study manifesto and the PhD Notebook application highlight the courses scheduled for the academic year, with indication of the corresponding CFU
    • by attending courses (and passing of possible final tests) in the area and discipline of reference organized by other universities/research institutions/businesses. The Teaching Board evaluates suitability of these activities with respect to the student’s training and research objectives and decides the number of credits to be assigned. For this purpose, the PhD application Notebook provides doctoral students with guidelines for choosing a course eligible in this category and its subsequent accreditation.
  • at least 4 ECTS
    • with the attendance (and passing of possible final tests) of transversal courses/seminars, chosen from those scheduled annually by the University in the areas of Cognitive and Interpersonal, Mobility, Enterprise, Research, Career Development and Communication. Courses scheduled for the current year, with indication of the credits awarded, are available at the link https://www.uniud.it/it/ricerca/bacheca-ricercatori/iniziative-ricerca
    • with the frequency (and passing of possible final tests) of courses/seminars/conferences organized by the University. The Teaching Board evaluates the suitability of these activities with respect to training and research objectives of the student and establishes the number of credits to be assigned
    • by attending courses (and passing of possible final tests) of transversal events organized by other universities/research institutions/companies. The Teaching Board evaluates the suitability of these activities with respect to the objectives of training and research of the student and establishes the number of credits to be awarded to assign.

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

  • Automated Software Testing, board contact: Vincenzo Riccio
  • Automated Algorithm Selection in Optimization and Machine Learning, board contact: Luca Di Gaspero
  • Computer vision for biodiversity monitoring and conservation, board contact: Niki Martinel
  • Optimized programming for real-time applications and beyond, board contact: Federico Fontana
  • Requirements Engineering, board contact: Angelo Susi
  • Responsible AI Through the Lens of an Information Access Researcher: The Good, the Bad, and the Unknown, board contact: Kevin Roitero
  • Full details in the PhD Notebook
  • Attendance of the courses at the Scuola Superiore (School for Advanced Studies of the University of Udine) in general is not elegible for obtaining credits. Please check eligibility of a course before attending.
  • AI for 3D digital heritage, board contact: Fabio Remondino
  • 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.