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.
It is important that you always check the official admission pages for admission dates. Usually, the School fills PhD places two times a year with the following approximate timelines:
Main admission in Summer
- Application Opens: beginning of April
- Application Deadline: beginning of July
- Interview: middle of July
- Start of Studies: 1st of September
Additional admission in Autumn
- Application Opens: Middle of September
- Application Deadline: beginning of November
- Interview: middle of November
- Start of Studies: 1st of February
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.
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: firstname.lastname@example.org.
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
We usually have admission for project-based and open places. Below is some general information on these types of places.
You can use this Template to write your Research Proposal.
Project-based PhD topics
The School of Digital Technologies offers project-based research topics for PhD students. These are announced in March and application will have a deadline in the summer (usually at the beginning of July).
To apply for a project-based position, you will have to get in touch with the contact person mentioned in the call (the supervisor) as early as possible, and get his/her agreement that you may go forward with the application. Usually, the supervisor will require a CV as well as a motivation letter that details how your previous experience allows you to address the research mentioned in the call. Usually there will be several meetings with the supervisor before the application is submitted in the summer.
Available project-based places (link will open in April 2020)
Open Research Topics at the School of Digital Technologies
Even for applying with an open topic, its is critical that your topic is aligned with one of the ongoing or future research projects. This is because most of our research students are financed through these projects. This alignment happens in close interaction with a supervisor at the school. It is therefore important that you start early and plan for several iterations of your research proposal until it is in a state that it can be submitted. Depending on your own availability and the availability of your supervisor, this process takes at least one month, sometimes more.
To define the topic and find a supervisor follow the following steps:
- Read the information on this page carefully
- Try to identify a research topic or a research area that your research interest is aligned to
- Prepare a short (2-3 page) description of your initial research idea
- Send your description to the head of the IST curriculum Prof. Tobias Ley (email@example.com) and he will try to identify a prospective supervisor. Of course, you can also contact a supervisor directly.
Once a supervisor has agreed to help you prepare the research proposal, continue with her or him.
The School of Digital Technologies is made up of five academic areas all of which offer research topics for PhD students. Information on each Academic Areas area can be found below:
We explore human learning at digital learning ecosystems. Our approach targets socio-technical systems as co-optimized social and technical sub-systems where different stakeholders perform tasks, interact, learn and create knowledge.
Our conception of learning takes ecosystemic approach focusing at mutually adaptive learning at individual and organization or culture level. We look at learning and knowledge building at different levels of stakeholders - learners, facilitators of learning, institutions and communities. We investigate how digital transformation facilitates the goals of individuals, organizations or communities as the learning ecosystem.
Our research contexts encompasses both designing and exploring formal learning situations at kindergartens, schools, universities or adult learning organizations, and the informal and non-formal learning outdoors, at museums, workplaces and citizen science activities.
Area: New Learning Practices, Tools and Environments
There is a current shift to new types of teaching and learning paradigms in schools and universities focussing on problem-based, collaborative and creative learning. Technological innovations in the area of mobile and social technologies are increasingly adopted in formal and informal education to support these practices. All topics should contribute to the creation of an interoperable ecosystem of learning tools that can be applied in practice. The typical research strategy you would employ is design-based research that involves stakeholders into the research process.
We constantly extend the types of learning contexts that we focus on. Latest interests include: Technologies and tools for workplace learning, knowledge sharing and knowledge management, learning in Industry 4.0, Technologies for Smart Schools, Technologies for learning and knowledge sharing in museums and outdoor classrooms and Game-based Learning.
Area: Data-driven and adaptive learning technologies, Learning Analytics
One special application domain of data analytics is in the area of Learning Analytics, where close collaboration to other researchers and PhD students in the learning domain will be required. In this domain, technologies allow gathering of more fine granular and timely data about learning processes. Feeding back this data to teachers and learners in the learning process in a sensible way holds great potential for improving learning and teaching processes. However, new types of learning paradigms that foster collaborative, creative and problem-based learning require a fresh perspective on learning analytics and educational data mining.
See here for more information: CEITER Learning Analytics and Educational Innovation
Area: Institutional Change and Scaling
How to move institutions (schools, universities, enterprises) to adopt new learning and knowledge sharing practices connected with digital tools is of paramount importance for driving innovation in the current digital economy. Often several barriers exist to widespread adoption, at the same time, there are new ideas for participatory and stakeholder-driven processes of innovation. Example contexts/focuses for Institutional change are: transfer to digital textbooks, BYOD, open classroom; self-directed learning, MOOCs/digital portfolios in education, workplace learning, cross-institutional learning.
Specialization could be in
- Assessing competence in digital transformation on an institutional level
- Participatory methods and open innovation systems for digital transformation
- Design, implementation and evaluation of interventions into current practices
- Studying how techno-economic systems adapt to new socio-technical learning regimes and what factors influence the development and uptake (or appropriation) of educational socio-technical innovations
Area: Cultural data analytics
Contact: Kai Pata (firstname.lastname@example.org)
Computational social science with open cultural data
The position relates with the ERA chair project of cultural data analytics. We search particularly people who have abilities in programming for algorithms that aid cultural data analysis. These data range from open digital archive data, to modern digital culture in media (texts, artifact metadata, tags, visuals, audio, video, digital media). The PhD student should preferably build the thesis on the improvement of algorithms and methods how to analytically make use of and visualise such data to make them differently reusable for the public or commercially provided cultural services, or for understanding culture new ways based on data in the field of computational social science.
Public engagement services with open cultural 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.
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.
Contact: David Lamas (email@example.com)
Trust has shown to be a key factor influencing user uptake and acceptance of technologies. Despite the increase in interest in trust research and its stated importance in HCI, prior research has mainly focused on understanding its role in human to human interactions mediated through technology. The ongoing and rapid technological developments have made it necessary to move beyond studying trust relationships between people mediated by information technology and focus on studying the relationship of the user with the IT artifact itself. We recognize that HCI as a discipline lacks a focused body of knowledge on trust and there is a lack of theoretically grounded and robust instruments for quantifying trust.
Possible topics in this area are:
- Theory and scale development
- Exploring physiological correlates of trust
Area: Neurophysiological art
This are combines computer science, neuroscience, engineering, design, performative arts and biohacking. Specifically, we are working on a specific type of interactive theatre where audience and actors can communicate through brain and neural computer interaction (BNCI) interfaces using multimodal sensors and actuators.
Possible topics in this area are:
- Neurocinematic system (database cinema/narrative engine)
- Neuro/bio-choreography (using wearable physiological sensors to augment performer bodily expressions, also can involve vibration inputs)
- Affective / emotional scripts (using principles of emotional dynamics to shape spectators interactive experience)
Area: Body-centric interactions
The goal of the work in this area is to bring together knowledge from the domains of kinesiology, personal informatics, embodied cognition, behavior psychology, human-computer interaction, and learning sciences into a united framework to be used to improve one’s well-being
Possible topics in this area are:
- Using multimodal data from body movement for feedback/perceptualization (visualization, sonification, tactile input) with the end goal of learning/correcting movement patterns or altering body perception
Research interests are in information culture and information practices in organizations, both in public and private organizations. It includes leadership and management aspects, teamwork and information-related competencies (information, media, digital and data literacy).
Contact: Peeter Normak (firstname.lastname@example.org)
Other supervisors: Pille Eslon, Merja Bauters, Arto Lanamäki
Area: Language Technology
Focus on intelligent computer-assisted language learning (ICALL) and natural language processing (NLP) applications in sociocultural and educational context (e.g., discourse and sentiment analysis, media studies).
The research bases on the Estonian Interlanguage Corpus (http://evkk.tlu.ee/). The aim is to broaden the current research in modeling of language proficiency levels of written L2 Estonian, and to achieve synergy.
Sub-area 1: IT-Applications in Language Education
- Comparison of first language (L1) and second/foreign language (L2) proficiency development: IT applications for pedagogical purposes and ICALL
- Exploring the role of language environment (i.e., comparison of second and foreign language acquisition) for pedagogical purposes and ICALL
- Analysis of academic L1 and/or L2 for developing e-learning and writing assistant tools
- Comparison of the writings and the writing process of L2 learners with different native languages for:
- automatic L1 detection
- developing specific study materials, i.a., ICALL solutions
- Modeling L2 learners’ text comprehension and reading process (e.g., by using eye tracking) for:
- language proficiency assessment
- developing level-specific study materials, i.a., ICALL solutions
- Automatic processing of L2 speech for:
- comparative study of learners’ speech production on different language proficiency levels
- developing ICALL applications for speech training
- Psycholinguistic analysis of the (unconscious) perception of cross-linguistic similarity, implications for pedagogical purposes and ICALL
- Computational modeling results of the psycholinguistical measurement of similarities between Different Language Use
Sub-area 2: Developing ICALL tools specific to language proficiency level
- Language tools to benchmarking L2 aquisition on different language proficiency Levels
- Automatic detection of suitable reading material for L2 learning contexts
- Automatic generation of L2 learning activities with level-specific difficulty
Sub-area 3: Management of Big-Data Resources: Strategies, Development and Applications
- Management and sharing of linguistics data resources: strategies, development and applications
- Applying artificial intelligence in opinion mining (solutions for Estonian language)
The preferred language to be studied is Estonian, however, the dissertation may focus on other languages.
Area: Digital Transformation
Digital transformation is a systemic technology-induced change covering all aspects of human life. It studies the potential benefits and benefits of emerging technologies that could overcome fundamental individual, organizational and societal limitations as well as the ethical limitations of adopting such technologies. It addresses micro, meso and macro-level challenges of digital transformation while exploring boundary-breaking innovations as part of the digital transformation that will enable ambitious and interactive human-technology development in intelligent socio-technical systems. This entails, for instance, an emphasized concern with how people react, change and integrate technologies in their everyday lives and on workplace. Main aim is to understand how stakeholders can be brought in as active participants through participatory processes in defining how emerging technologies (e.g. artificial intelligence) shape the society and the future. This will ensure that future e-services are trustworthy, safe and sustainably provide values for all stakeholders.
Topics: are agreed between the applicant and the potential supervisor.
Contact: Madis Lepik (email@example.com)
Area: Didactics of Mathematics
Contact: Madis Lepik (firstname.lastname@example.org)
Area: Topological algebras
Contact: Mart Abel (email@example.com)
Topic: Topological Segal algebras
The study of some particular kinds of Segal algebras started already in 1930-s, in 1965, the term “Segal algebra” was introduced. Due to several applications of some particular Segal algebras in several areas of mathematics (time-frequency analysis, frames, spectral syntehsis, mathematical formulation of mathematical physics, etc.), the topic become popular in 1970-s. During last few decades, there have been published many papers on different kinds of Segal algebras (mainly with C*-algebras, Frechet algebras, etc.) In 2016, the concept of general topological Segal algebras (instead of C*-algebras or Frechet algebras, one considers here any real or complex topological algebra) was introduced by Mart Abel. Therefore there is a lot of results to generalise and to prove for the case of general topological Segal algebras. The proposed PhD Thesis would be a deeper study of topological Segal algebras in general.
Area: Approximation methods of functions and applications in signal analysis
Contact: Andi Kivinukk (firstname.lastname@example.org)
Topic: Non-standard trigonometric Fourier analysis and Shannon sampling series
Kramer’s sampling theorem gives a general approach for various sampling theorems [see, e.g., J. R. Higgins, Sampling theory in Fourier and signal analysis. Oxford, 1996]. The proposed PhD project would be study a non-standard trigonometric Fourier series and corresponding sampling series deduced by Kramer´s theorem.