Below information is prepared for students interested in applying for the PhD programme in Information Society Technologies at the School of Digital Technologies at Tallinn University.

What to do and whom to contact

The admission process involves two parallel processes that need to be followed. It is important that you start early with both these processes because both take some time.  

  1. The general admission process that is dealt with by the central admission unit at Tallinn University:

  • Extensive information on what the formal requirements are, what to submit for the application and about deadlines can be found on the general information page of the PhD program. Here you can also find details about the curriculum.

  • In case you have any questions about any of this, including also language requirements, visa requirements and where to send the application should be directed to:

  1. Finding a research topic and a supervisor is dealt with by the School of Digital Technologies.

  • When you apply for a PhD, you will be required to submit a research proposal together with an agreement by a potential supervisor. Below you find information about this process.  


Finding a topic and a supervisor

Current admission is open for two project-based research topics, which call will be opened in September 2019.


Research Topics at the School of Digital Technologies

The School of Digital Technologies is offering two project-based research topics for PhD students. Information can be found below:

Public engagement services with open cultural data
User modelling and user-adapted interaction


The following recording and materials also contain presentations of these areas and the topics they offer.

You can use this Template to write your Research Proposal.


Public engagement services with open cultural data

Contact: David Lamas (, Kai Pata (, Sirje Virkus (

This PhD position calls for exploring the issues of curating cultural data to enhance its usability in new public services; designing and testing out the services based on open cultural data; developing the algorithms to process cultural data for specific services needs such as for creating awareness, enabling crowd collaboration and engagement based on aggregated dynamic open data.

The government bodies, as well as supervised public bodies, are going to publish as open data the cultural data that falls within the definitions of public information. ‘Open cultural data’ is data from cultural institutions that is made available for use in a machine-readable format under an open licence ( e.g. language data, media data, cultural heritage data, literature data corpora). Open data is a new digital commons, a resource to which citizens are entitled and that must be delivered qualitatively. These data sets are provided for re-use by citizens, academic institutes and enterprises in order to contribute to the development of the national cultural product. It is envisioned that open data could be creatively repurposed, aggregated or augmented with other data sources in the context of evolving data infrastructures which are attuned to the specific needs and interests of civil society actors. Open cultural data supports discovery, reuse and innovation in digital humanities.

By generating transparency, open cultural data can feed civic agency such as social mobilisation, informed decision-making and behavioural change increasing wellbeing and sustainable actions based on feedback loops from dynamic data. New potential services that use open data may promote data driven engagement of the crowds for empowering citizens, and solving public problems. For example open city spaces may be made smart, by enabling decision-making, engagement and interaction opportunities in shared spaces fuelled by open cultural data.


User modelling and user-adapted interaction

Contact: David Lamas (, Aleksander Väljamäe (

Personalization techniques, in general, build upon user models. These models are application specific and account both for long term user properties (e.g. preferences, attitudes, personality traits, which are stable over longer time periods) and short term User properties (e.g. affective/cognitive states, which can change more rapidly). Long term properties can be acquired with existing acquisition techniques using either one-time Intrusive questionnaires or slowly and unobtrusively via various modalities, e.g. ratings, browsing history, social media streams. However, these approaches fail for short term Properties, which change rapidly. Hence, personalization techniques still lack quick, responsive and unobtrusive techniques to acquire the short term user properties.

The measurement of peripheral physiology and brain responses of users can complement the traditional user feedback acquisition techniques by providing more insight into short term changes of the user. Physiological measurement can be continuously available, quantitative and relatively unobtrusive. Therefore, personalized systems can now aim for a more detailed and temporally adaptive user models that try to mimic the dynamics of the user’s cognitive and affective states. In addition, the physiological data can also be used for complementing long-term user properties (e.g. personality traits). Furthermore, physiological measures can be combined with behavioural data to provide a more detailed multimodal model of the user.

Research Areas and PhD Topics

PhD studies will concentrate on the research embracing:  

  • Development of physiological user models for personalized systems
  • Collection of datasets with physiology information in personalized systems/human-computer interaction
  • Enhancing user/learner models with physiology;
  • Evaluation of physiology-based personalized services;
  • Design, development and validation of novel applications considering physiology including games, cinema, theatre, multimedia content, and social media.