To advance from the first to the second year, the doctoral student must acquire at least 30 ECTS from the first year, have an approved doctoral dissertation topic, and submit the disposition of the doctoral dissertation, as evidenced by the mentor’s signature on the form OBR-FIŠ-034.

The condition for advancing from the second to the third year is the completion of all obligations of the first year, amounting to 60 ECTS, including a confirmed disposition, and the completion of individual research work for the second year, amounting to 60 ECTS.

The competent authority may allow the student to advance to a higher year, even if the required conditions are not met, in the following circumstances: maternity, prolonged illness, exceptional family or social circumstances, and/or participation in top-tier cultural, sports, or professional events. In such cases, the doctoral student must provide appropriate evidence.

A doctoral student who does not meet the conditions for enrolling in a higher year may repeat a year once during their studies or change their study programme or course due to non-fulfilment of obligations in the previous study programme or course.

The conditions for completion of the study programme are following:

• Successful completion of the study obligations prescribed by the programme;
• Publication or acceptance for publication of one scientific paper, for which the candidate is the first or the last author, in a journal indexed in SCCI, SCI, SCOPUS (with IF>0), or AHCI, in the research field of the doctoral thesis;
• Preparation and successful defence of the doctoral dissertation.

A doctoral student who enrols directly into a higher year must complete all required additional exams and regular study obligations of the second and/or third year in order to finish the study programme.

Doctoral students of the study programme Information Sciences will obtain the following general and subject-specific competences:

General competences

• The ability to identify a given research problem, analyse it, evaluate it, and develop possible solutions.
• The creation of new knowledge, contributing significantly to the advancement of science.
• The ability to master standard methods, procedures, and processes of research in the scientific field of study.
• The ability to conduct independent research and development work and lead a research group.
• A commitment to the quality of scientific research through autonomy, (self)criticism, (self)reflection, and (self)evaluation.
• A dedication to professional ethics.
• The ability to solve specific research problems within a particular scientific field.
• The development of skills in applying knowledge to the research area of the doctoral dissertation.
• The ability to innovate through the use and combination of various research methods.

Subject-specific competences of obligatory courses

Doctoral Seminar 1:

• Ability to autonomously solve concrete research problems by analysis, choosing appropriate methodology and developing the solving process.
• Ability to operationalise abstract theoretical concepts.
• Formulation of the research design and the ability to defend it.
• Ability to prepare a scientific paper, present it and conduct peer reviews.

Doctoral Seminar 2:

• Ability to plan and autonomously solve concrete research problems, including the most complex issues.
• Ability to operationalise abstract theoretical concepts at the advanced level.
• Critical evaluation and formulation of the research design at the most advanced level, and the ability to defend it.
• Ability to prepare a doctoral dissertation, present it and conduct peer reviews.
• Ability to acquire, interpret, select, evaluate and insert new knowledge and the ability to interpret it.
• Ability to present the obtained scientific results.

Introduction to Scientific Research:

• The ability to identify a relevant scientific research problem and to prepare a research plan for doctoral research.
• Skills in scientific writing and the ability to present obtained results in the relevant research field.
• Understanding the dynamics of scientific work and publishing at the international level.

Modelling and Analysis of Complex Networks:

• Creating the solutions of research problems in network analysis.
• Ingenuity in identifying research problems that can be formulated as network science problems.
• Extracting scientifically relevant information from a network via most suitable methodology.
• Use of standard software for network analysis and of software packages for specific methods, such as community detection.

Modelling, Analysis, and Learning from Data:

• Apply statistical analysis techniques: use statistical methods for data transformation and preprocessing, as well as for transforming random variables.
• Develop machine learning models: demonstrate competence in constructing and refining algorithms for various machine learning tasks, and analyzing real-world and laboratory big data using supervised, semi-supervised, and unsupervised techniques.
• Think critically and interpret data analysis results.
• Develop AI-based generative models.
• Proficiency in using topological techniques to study the shape and features of data and handle multi-dimensional data through topological frameworks.
• Ability to use statistical methods for forecasting: employ statistical techniques to predict future data points based on previously observed data, and model dependencies and trends over time in datasets.
• Ability to effectively integrate methods and insights across various scientific fields.
• Recognise and adjust to the unique data requirements and structures inherent to each scientific discipline.
• Critically assess a wide range of methodological approaches from data and computer science and apply the most suitable ones.
• Evaluate the realistic limitations of considered methods and identification of potential areas for improvement.

Individual Research Work 1:

• Ability to solve concrete research problems in individual scientific fields.
• Development of skills and abilities for using knowledge in the scientific field of doctoral dissertation.

Individual Research Work 2:

• Ability to evaluate the obtained research results and put them in the context of the entire doctorate.

Individual Research Work 3:

• Ability to evaluate the obtained research results and put them in the context of the entire doctorate.
• Mastery of presenting the obtained research results to the scientific audience.

In accordance with the Decree on Co-Financing Doctoral Studies (Official Gazette of the Republic of Slovenia, no. 22/17) and the amendment to the Higher Education Act, doctoral studies at the Faculty of Information Studies in Novo mesto are co-financed from the 2017/18 academic year onwards.

An important novelty is that candidates do not apply for the co-financing call, as co-financing will be available to all doctoral students who meet the following conditions:

• Students enrolled in the first year and directly in the second year.
• Students enrolled in the second year of doctoral studies will be co-financed if they have regularly advanced from the first to the second year of doctoral studies. An extension of the status for justified reasons in accordance with the law governing higher education and the Statute of the Faculty of Information Studies is also considered as regular advancement.
• Students enrolled in the third year of doctoral studies will be co-financed in the event that they have regularly advanced to all the years of doctoral studies. An extension of the status for justified reasons in accordance with the law governing higher education and the Statute of the Faculty of Information Studies is also considered as regular advancement.
• In the event that doctoral students have already obtained a level of education corresponding to the level of education obtained in the doctoral study programmes of the third level, they will not be eligible for co-financing. Furthermore, in the event that they have or have already had publicly co-financed study under study programmes for obtaining a
  doctorate, even if they have not completed their studies, they will not be eligible for co-financing.
• Parallel studies are not co-financed, nor are students enrolled in an additional year (absolventski staž) eligible for co-financing.

The amount of co-financing for an individual student depends on the number of enrolled students and the amount of funds that we receive each year for this purpose.

Co-financing is not excluded by regular employment. The same applies to registration in the register of unemployed persons at the Employment Service of Slovenia.

There is no age limit for co-financing.

Interested candidates should register at referat@fis.unm.si.