This 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.

Admissions

You must check the official admission pages for admission dates. Usually, there are two admission periods, in Summer and winter.

These are the important dates for the next period of admission: 

  • Admissions for the academic year 2022/2023 open on May 23
  • The application deadline is June 30
  • The admission interview is scheduled for July 6
  • Studies start on the 5th of September 2022

What to do and whom to contact

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

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

  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 endorsed by a potential supervisor. Below you will find information about this process.  

Finding a research topic and a supervisor

There are project-based and open research topics:

  • Project-based research topics are well identified and usually have predefined supervisors.
  • Open research topics are related to the broad research areas within the School of Digital Technologies.

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

 

Project-based research topics

Project-based research topics are announced yearly. The description of the project-based research topics available for the academic year 2022/2023 is available here.

To apply with a project-based research topic, you will need to contact the proponent of the topic, your prospective supervisor, to get her or his agreement that you may go forward with the application. 

Usually, the supervisor will ask for your curriculum vitae as well as a motivation letter. Please make sure these emphasize how your previous experience allows you to address the project-based research topic you intend to apply for. Usually, there will be several meetings with the supervisor before the application is accepted and submitted.

 

Open research topics

Open research topics are related to the broad research areas within the School of Digital Technologies so it is critical that your topic is aligned with existing or planned areas of research.  

Aligning your interests with existing or planned areas of research in close interaction with a potential supervisor at the School of Digital Technologies

It is therefore important that you start working on your research proposal early and plan for several iterations until it is in a state that it can be submitted. Depending on your availability and the availability of your potential supervisor, this process takes at least one month, but often more.

Take the following steps to set your topic and find a supervisor: 

  1. Read carefully the information about open research topics presented below;
  2. Identify the area of research closest to your research interests;
  3. Prepare a short two to three pages description of your initial research ideas; 
  4. Send your description and your curriculum vitae to the head of the curriculum, David Lamas (david.lamas@tlu.ee), so that he can help you identify a prospective supervisor, or contact them directly.  

Once you found a prospective supervisor willing to help you prepare your research proposal, continue working with her or him.

Information on each of the research areas of the School of Digital Technologies can be found below:

Technology-Enhanced Learning

Contact: Kairit Tammets (kairit.tammets@tlu.ee)

Other supervisors: Terje Väljataga (terje.valjataga@tlu.ee), Kairit Tammets, Maria Rodriquez, Luis Pablo Prieto, Tobias Ley (tobias.ley@tlu.ee), Mart Laanpere (mart.laanpere@tlu.ee), Martin Sillaots

We explore human learning in 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 an ecosystemic approach focusing on mutually adaptive learning at the individual and organization or cultural levels. 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 encompass 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 in the research process.

We constantly extend the types of learning contexts that we focus on. The 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 How we do research

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, and 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 (kai.pata@tlu.ee)

Computational social science with open cultural data

The position relates to 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, artefact 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.
 

Human Computer Interaction

Contact: David Lamas (david.lamas@tlu.ee)

Other supervisors by alphabetical order: Nuno CorreiaMati Mõtus, Sónia Sousa, Vladimir Tomberg.

Within the field of human-computer interaction, we focus on advancing knowledge about how people perceive and interact with information technologies and how to further develop these technologies to support and augment their individual and collective physical, perceptual and cognitive abilities.

Research streams are:

  • Design theory and methodology
  • User experience
  • Novel human-computer interaction modalities

Area: Design theory and methodology

Over the past three decades, we have witnessed shifts, connections, and re-framings in just about every area of interaction design: how it is done, who is doing it, for what goals, and what its results are. These changes show shifts from designing things to designing interactions, first on micro-level and lately also on a macro level; and from designing for people to designing with people and very recently, to designing by people. We focus on empowerment and on enabling a new wave of digital literacy as possessing the knowledge, skills, and attitude to use our digital environment is no longer enough, we need to be able to shape it.

Possible topics:

  • Exploratory research studies developing new knowledge about we (facilitate the) design of digital artefacts
  • Design and development research studies creating new design methods that are meant to improve in a specific way some activity in the way we design digital products and services

Area: User experience

User experience is an emerging research area with a range of issues to be resolved. Among them, the measurability of the user experience remains controversial. Critical arguments hinge on the meaningfulness, validity, and usefulness of reducing fuzzy experiential qualities such as fun, challenge and trust to numbers; and ongoing beyond using user’s perceptions, actions, and reactions as raw data, to using neurophysiological responses as data for measuring user experience. Ongoing work focuses on trust and on hedonic aspects of user experience. 

Possible topics:

  • Theory and scale development
  • The study of neurophysiological correlates of user experience

Area: Novel human-computer interaction modalities

This area combines computer science, neuroscience, engineering and design. Our main focus is on the design and development of novel human-computer interaction modalities. As most of the human-computer interaction modalities we explore rely heavily on personalization techniques, we also focus on the definition of user models and interaction adaptation techniques using neurophysiological computing.
 
Possible topic:

  • Design, development, and validation of novel human-computer interaction modalities
  •  

Information Science

Contact: Sirje Virkus (sirje.virkus@tlu.ee)

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).

Applied Informatics

Contact: Peeter Normak (peeter.normak@tlu.ee
Other supervisors: Pille Eslon, Merja Bauters and 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. The aim is to broaden the current research in modeling of language proficiency levels of written L2 Estonian, and to achieve synergy.

Sub-area: 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: Developing ICALL tools specific to language proficiency level

  • Language tools to benchmarking L2 acquisition 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: 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.

Subarea: methodological design in the area of digital transformation

Contact: Merja Bauters (merja.bauters@tlu.ee

For any transformation to occur there is a need to change existing practices in all of the above-mentioned levels (micro-, meso- and macro-level). The need to change is often felt to be challenging and suspicious, which hinders and narrows the visions of future possibilities. Therefore, we need to design and develop new methods for all the phases of the transformation process – the future-oriented contextual inquiry, design & prototyping and longitudinal appropriation evaluation. It would be beneficial if the research could take into account the new knowledge on human learning (neuroscience on learning) and the effect of art and culture in learning. The themes can form on the level of the design process (the future-oriented contextual inquiry, design & prototyping and longitudinal appropriation evaluation) or vertically on the levels of micro-, meso- and macro-level or these can be combination of the design process and transformation levels e.g. future-oriented contextual inquiry cutting through micro- and meso- level.

Subarea: Deskilling among platform-dependent contractors

Contact: Arto Lanamäki

Due to the widespread application of digital technologies in professional life, practically all types of work have become digital (Orlikowski & Scott, 2016). This is especially true in the context of digital platforms that increasingly mediate, facilitate and govern work (de Reuver, Sørensen, & Basole, 2018). The platform economy has introduced the proliferation of micro-entrepreneurs and platform-dependent contractors who perform a variety of tasks ranging from ride-hail driving to short-term home rental to food delivery to e-scooter charging (Kuhn & Maleki, 2017). These occur in the conditions of algorithmic governance and information asymmetries (Kellogg, Valentine, & Christin, 2020; Rosenblat & Stark, 2016), under the larger framework of surveillance capitalism (Zuboff, 2015). Simultaneously, work life has become more diverse and insecure, increasingly deviating from the older ideal of “lifelong full-time work organized in a single industrial location” (Beck, 1992, p. 143).
Platform-dependent contractors (PDCs) represent a little-investigated demographic. In this doctoral research, the aim is to conduct a longitudinal study of deskilling among PDCs. The concept of deskilling refers to technology turning skilled manual labor into mechanized work with increased managerial control (Edgell & Granter, 2019). The concept originates in the work of Braverman (1974) and is highly debated (Attewell, 1987; Ertürk, 2019). The purpose of this PhD project is to identify the long-term impacts of digital platforms on service work: does it cause deskilling? If yes, what could be done about it?
This is a four-year fully funded doctoral researcher position. The candidate should have good social skills, be an excellent writer and communicator, and motivated in building an international research career. It is required that the doctoral researcher takes her/his own initiative and is responsible for timely progress. The candidate should have a background in social sciences and technology, for example in the fields of information systems, human-computer interaction, urban studies, communication, organization studies, or similar. The doctoral researcher begins the work in September 2020, or as soon as possible after that. The goal is to submit the final thesis for review by the end of 2023 and defend the thesis before the summer 2024.
The applicant is free to suggest which type(s) of platform-dependent contractors (s)he is going to study. The doctoral researcher will be provided additional funding necessary to conduct the data collection during an extensive fieldwork. The position is associated with the digital transformation research team at the Tallinn University.

Methods to be applied and instrumentation used
The candidate should have a good understanding of qualitative research methods. Long-term ethnographic fieldwork is preferred (see, for example, Leonardi, 2015; Rosenblat, 2018; Van Maanen, 2011).

Mathematics and Didactics of Mathematics

Contact: Madis Lepik (madis.lepik@tlu.ee)

Area: Didactics of Mathematics

Contact: Madis Lepik (madis.lepik@tlu.ee)

Area: Topological algebras

Contact: Mart Abel (mart.abel@tlu.ee)

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 synthesis, 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 (andi.kivinukk@tlu.ee)

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 to study a non-standard trigonometric Fourier series and corresponding sampling series deduced by Kramer´s theorem.