Information and communication technology is one of the fundamental pillars of development and innovation in various aspects of the economy and society. Informatics is an interdisciplinary field connected to various areas of science. In the long term, it represents one of the most promising and fastest-growing segments of economy.
Doctoral Study Informatics was launched due to the expressed need for educated professionals at the highest level who can creatively respond to all the challenges of the development of information and communication technology, taking into account the latest scientific knowledge. Ultimately, informatics, with its multidisciplinary connections to other scientific fields, provides appropriate scientific solutions aimed at increasing the efficiency, rationality, and safety in the field of information technology application for the benefit of social development. Therefore, the Doctoral Study Informatics introduces students to scientific research and provides a foundation for the development of new methods and techniques for solving real problems, as well as the adoption of knowledge that contributes to the progress and wellbeing of society as a whole. Starting from the academic year 2022/2023, a modernized Doctoral Study Informatics is being implemented, which is closely connected to the research programs of the laboratories at the Faculty. Additionally, an International Council of the Doctoral Study Informatics has been established, ensuring international recognition and continuous opportunity to monitor the quality of the research work of doctoral students and mentors by internationally recognized scientists.
Excellent cooperation within the study program has been achieved with doctoral students from the industry and non-academic sectors, who are researching the challenges of their own business domains, providing an opportunity to apply competencies and expand knowledge on problem tasks from various application areas, so that we can grow, develop and strengthen together through knowledge transfer. We believe that potential students will recognize our values and decide to enroll in the doctoral program at the Faculty of Informatics and Digital Technologies at the University of Rijeka.
Basic information
- name of study program: Doctoral Study Informatics
- program host: Faculty of Informatics and Digital Technologies
- program executor: Faculty of Informatics and Digital Technologies
- duration / ECTS credits: 6 semesters / 180 ECTS credits
- enrollment quota: 10
Admission requirements
- completed university graduate study in informatics, computer science, mathematics, physics, polytechnics, or other related studies in the fields of technical, social, or natural sciences (provided that the candidate has achieved 300 ECTS credits, including undergraduate studies)
- completed university undergraduate study based on a related study program in the fields of technical, social or natural sciences, started before the Law on Scientific Activity and Higher Education (NN, no. 123/03) was implemented
- a master’s degree in relevant fields of social, technical or natural sciences
Enrollment dynamics/dates
The application period opens in June, the deadline for applications is in early October, and enrollment is in late October.
Tuition fee and payment method
6.905,00 EUR (payable in 6 instalments: full-time students – two instalments per year, part-time students – one instalment per year)
More information
Head of Doctoral Study Informatics
Prof. Sanda Martincic-Ipšic, PhD
Program website:
https://inf.uniri.hr/en/study-programmes/university-postgraduate-doctoral-studyinformatics
Contact:
doktorski@inf.uniri.hr or +385 51 584 771
Our doctoral students
| Year of obtaining a Ph.D. degree | Name and surname of the student/employer | Ph.D. thesis topic |
|---|---|---|
| 2021. | Mate Krišto, PhD, / Croatian Ministry of Internal Affairs | Human Detection and Recognition in Harsh Conditions Using Infrared Thermal Vision |
| 2020. | Petar Jurić, PhD, / Primorsko-goranska County | An M-learning System Model Based on Stream Data Mining |
| 2020. | Maja Gligora Marković, PhD, / Faculty of Medicine in Rijeka | The System for Dynamic Generation of Learning Objects as Support for Individual Personalized Teaching |
| 2019. | Slobodan Beliga, PhD, / Faculty of Informatics and Digital Technologies | Keyword Extraction Based on Structural Properties of Language Complex Networks |
Research questions/hypotheses addressed in defended doctoral theses
| Research questions/hypotheses | Name and surname of the doctoral student |
|---|---|
| H1. The application of infrared thermographic systems enables increased protection and security of protected spaces. H2. The use of infrared thermography enables reliable detection and recognition of persons in challenging weather conditions. H3. Functions can be achieved in real-time, thus allowing for reduced need for continuous human supervision. | Mate Krišto, PhD |
| H1. Keyword extraction task can be successfully performed using the information incorporated in the structure of the complex networks of language. H2. Selectivity is a node level measure that is appropriate for the keyword extraction task. H3. It is possible to improve the performance of the keyword extraction method for the particular task, natural language, or domain by modifying the selectivity with the generalized selectivity measure. H4. The proposed method for keyword extraction is portable to different natural languages and different domains. | Slobodan Beliga, PhD |
| H1. The developed method for assessing the level to which a learning object meets adaptation criteria (cognitive style, learning style, learning goals) allows for reliable assessment of the value of learning objects according to established adaptation criteria. H2. Users of a system for dynamic generation of learning objects in which the selected multicriteria decision-making method is used for deciding on the application of adaptation criteria will achieve better knowledge acquisition than users who did not use that system. | Maja Gligora Marković, PhD |
| H1, Deep analysis of data streams can be used to detect gifted students in mathematics in mobile learning systems that are based on educational computer games containing problem tasks with predetermined values. H2, The use of additional motivational elements in educational computer games designed for learning mathematics affects the frequency of their use. H3, Gifted students post messages of different content type on a social network for learning mathematics than other students. H4, Students who are more active on a social network for learning mathematics achieve better results in mathematics than students who are less active. | Petar Jurić, PhD |