Admissions

Please read the official admission pages for admission dates. The main admission period is in the summer.

These are the important dates for the current admission period

  • The additional round of admissions for the academic year 2026/2027 opens on 5 October 2026
  • The application deadline is 18 October at 13:00 pm [Eastern European Summer Time (EEST) (UTC+03:00)]
  • Interviews will take place on 4 November 2026 over Zoom. Detailed information will be sent directly to the applicant.

Courses

The course list and nominal study plan for the Information Society Technologies doctoral programme for the 2026/2027 admission can be found here.

Admission process

The admission process involves two parallel processes: the process of finding a research topic and a supervisor and the general admission process. It would be best to start these processes early because both will take some time.  

  1. Before applying you need to have a research topic and a supervisor. The School of Digital Technologies supports you in finding a research topic and a supervisor.

  2. The Admissions Office deals with the general admission process at Tallinn University.

 Extensive information on the formal requirements, what to submit for the application, and deadlines can be found in the general information for the university's doctoral programs.

If you have any questions about this, including language requirements, visa requirements, and where to send the application, don't hesitate to contact admissions@tlu.ee.

Documents

The following documents must be submitted in the SAIS admissions system:

  • Application form (applications are accepted during the period announced in the academic calendar)
  • Doctoral research proposal approved by an academic referee qualified to supervise doctoral research
  • Curriculum Vitae (CV)
  • Motivation letter

Admission criteria

The admission criteria are:

  • General motivation (10 points)

  • Academic excellence (20 points)

  • Originality and independence (20 points)

  • Communication skills (20 points)

  • Preparedness for doctoral studies (30 points)

The minimum programme enrolment threshold is 75 points out of 100.

Application stages

Stage 1 – Document-based assessment (min 65 points). During the document-based assessment, committee members evaluate candidates based on the materials submitted with the application, including the motivation letter, curriculum vitae, academic transcripts, diplomas, certificates, and research proposal. The purpose of this stage is to assess the applicant’s overall academic preparation and readiness for doctoral studies, as well as the quality, clarity, originality, and feasibility of the proposed research.

Candidates obtaining a score below 65 points will not proceed to the interview stage. Candidates obtaining 65 points or more will be invited for interview.

Stage 2 – Interview-based assessment. The interview stage complements the document review by allowing committee members to assess aspects that cannot be fully evaluated through written materials alone, including motivation, critical thinking, communication skills, intellectual independence, and the candidate’s ability to engage constructively with feedback.

Doctoral students' positions at Tallinn University

Tallinn University has three kinds of doctoral student positions:

  • Junior Research Fellows – The university employs these doctoral students for the purpose of their studies. The nominal study period for a full-time position is 4 years. The exact workload and nominal study time depend on the individual study and research plan and can last from 4 to 8 years. Junior researchers devote 85% of their time to research, knowledge transfer and institutional activities and 15% to educational activities such as: supervising students at the first and/or second level of higher education; teaching at the first and second level of higher education; supporting educational activities in their field of teaching.  
  • Doctoral Students – These are doctoral students the university does not employ for their studies. The nominal study period depends on the individual study and research plan, lasting 4 to 8 years. Doctoral students do not receive doctoral allowances but selected activities can be supported by the universit's reserach fund.
  • Doctoral Studies in Cooperation with Companies and Public Sector Organisations – Tallinn University offers opportunities to carry out doctoral research in close collaboration with companies and public sector organisations. These doctoral studies are designed to support partners’ strategic development by addressing real-world challenges through research and development activities. The focus is on strengthening innovation capacity, supporting data-driven decision-making, and developing sustainable, knowledge-based solutions. Such collaboration enables organisations to integrate research into their everyday practices while contributing to the development of new knowledge, competencies, and long-term impact.

Finding a research topic and a supervisor

There are pre-defined and open research topics:

  • Pre-defined research topics are well-identified, have predefined supervisors and are usually associated to ongoing projects.
  • Open research topics are not pre-defined but should relate to the broad areas of research within the School of Digital Technologies. 

You can find additional information about both kinds of research topics below. Once you have identified your topic and a potential supervisor, You can use this template to write your research proposal.

 

Pre-defined research topics and positions

Pre-defined research topics and positions are updated for each admission period. The descriptions of pre-defined research topics for the admission period open from 5 October to 18 October, 2026, are available below.

To apply for a project-based research topic, you will need to contact the proponent of the topic, your prospective supervisor, to get their agreement to go forward with the application.

Usually, you will be asked for your curriculum vitae, a motivation letter and a research statement. Please ensure these emphasize your previous experience and how it allows you to address your topics of interest. Usually, there will be several meetings with the potential supervisor before the application is deemed ready to be submitted.

Computational Interaction

Contact: Vladimir Tomberg (vladimir.tomberg@tlu.ee)

Position: Junior Research Fellow

Topic: „Computational Interaction“

The doctoral position focuses on Computational Interaction and its application in Digital Behaviour Change Interventions (DBCIs) – technology-driven systems designed to support sustainable positive changes in human behaviour. The research combines human–computer interaction, data science, and behavioural science, focusing on the design of adaptive and personalised digital interventions.

The project may explore how dynamic user data, predictive analytics, and adaptive algorithms can be used to develop personalised and context-aware solutions that respond to users over time. Particular attention is given to creating evidence-based and scalable behaviour-change solutions.

This position is part of the HCI for Health research group and contributes to strengthening data-driven approaches in Human–Computer Interaction. The work aligns with Tallinn University’s strategic focus on high-quality research and digital innovation addressing societal challenges.

We are seeking a candidate with a background in a relevant field (e.g. HCI, data science, computer science, or related disciplines), an interest in behaviour change technologies, and the ability to work across disciplines. Experience with data analysis, machine learning, or interactive systems design is advantageous.

Psycho-Physiological Indicators of Creative Barriers

Contact: Mati Mõttus (mati.mottus@tlu.ee)

Position: Junior Research Fellow

Topic: Psycho-Physiological Indicators of Creative Barriers

The doctoral position focuses on identifying and analysing psycho-physiological indicators of creative barriers, often described as “creative blocks.” The research combines human–computer interaction, cognitive psychology, and physiological computing to better understand how complex mental states—such as stress, fatigue, and cognitive load–relate to creativity and problem-solving.

The project may explore how physiological signals, including skin conductance, pupil dilation, and facial expressions, can be used to detect and differentiate mental states over time. A particular emphasis is placed on identifying patterns that distinguish creative barriers from other conditions, such as fatigue or stress, and on understanding how these states vary between individuals. The research aims to develop a data-driven approach to modelling creativity-related processes and their temporal dynamics.

This position is part of the Human Factors research group within the Human-Computer Interaction research direction and contributes to advancing research in human-centred and data-informed interaction. The work supports the development of the research group and aligns with Tallinn University’s focus on digital and media culture, education innovation, and interdisciplinary research.

We are seeking a candidate with a background in a relevant field (e.g. HCI, cognitive science, psychology, neuroscience, or related disciplines), an interest in human factors and creativity research, and the ability to work across disciplines. Experience with physiological data collection or analysis, experimental research methods, or signal processing is advantageous.

Data-Driven Labor Market Forecasting and Skills Intelligence

Contact: Danial Hooshyar (danial.hooshyar@tlu.ee)

Position: Doctoral Student

Topic: Data-Driven Labor Market Forecasting and Skills Intelligence

The doctoral position focuses on the development of computational approaches for labormarket forecasting, with particular emphasis on integrating heterogeneous data sources and modelling their evolution over time. The research addresses how machine learning and data-driven methods can be used to anticipate shifts in skills demand, workforce dynamics, and emerging competencies in rapidly changing socio-technical environments. It combines perspectives from machine learning, data science, and labor market analytics.

The project may explore how diverse data streams such as job advertisements, labor statistics, and innovation indicators can be aligned and fused into coherent representations of labor market dynamics. Particular attention is given to handling data inconsistency, bias, and fragmentation, as well as to designing models that remain robust under changing conditions.

The research is also expected to investigate temporal aspects of forecasting, including how predictive models adapt to evolving trends and maintain stability over time.

This position contributes to ongoing research on data-driven decision support for education, training, and workforce development. The work aligns with Tallinn University’s strategic focus on digital transformation and supports the development of tools and methods for anticipating future skills needs and informing policy and organisational decision-making.

We are seeking a candidate with a background in a relevant field such as machine learning, data science, computer science, or related disciplines, an interest in labor market analysis and forecasting, and the ability to work across domains. Experience with time-series modelling, data integration methods, or predictive analytics is advantageous.