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
- The admission for the academic year 2020/2021 is open from 11 May 2020
- Application Deadline: 1st of July 2020
- 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: email@example.com.
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:
Enactive biofeedback-driven encounters with human-like artificial agents
User modelling and user-adapted interaction
Enhancing critical thinking and empathy for allowing a thoughtful change in practices towards sustainable and responsible future
Public engagement services with open cultural data
The position is available at the ERA Chair for Cultural Data Analytics (CUDAN), which is funded by the European Commission, and will be led by the designated ERA Chair holder, Professor Maximilian Schich.
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 (firstname.lastname@example.org) 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 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, 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
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.
Contact: David Lamas (email@example.com)
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
- Neurophysiological computing
- Body-centric computing
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.
- 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.
- Theory and scale development
- The study of neurophysiological correlates of user experience
Area: Neurophysiological computing
This area combines computer science, neuroscience, engineering and design for biohacking, here understood as measuring various biomarkers and behaviors for artistic expression or to optimize health and wellbeing. This relies heavily on personalization techniques, which in general, build upon user models and interaction adaptation techniques. Our focus is on modelling and implementing personalization techniques using neurophysiological computing. These models are application-specific and account both for long term user properties, which are stable over longer time periods (e.g. personal preferences, attitudes, personality traits, prevailing moods) and short term user properties, which can change more rapidly (e.g. momentary affective/cognitive states, feelings).
- The development of neurophysiological user models for personalized systems
- Design, development, and validation of novel applications of neurophysiological computing for the arts, health or wellbeing
Area: Body-centric computing
Computing is moving closer to our body, as reflected by the growing amount of research and commercial products. However, both the research agenda, vocabulary and technology for discussing, designing and developing for the body still needs to be shaped. Currently lacking are means of reasoning about how body-centric interfaces are assigned roles and meaning; models for predicting user intent when interacting with body-centric computing ecologies; and infrastructure for enabling seamless body-centric computing and dynamic substitution of inherent body-centric interfaces.
- Theories and models enabling reasoning about body-centric interactions
- The development of adaptive infrastructure for supporting dynamic reconfigurations of body-centric interface computing
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).
1. 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: 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.
2. 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
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
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).
Area: Didactics of Mathematics
Area: Topological algebras
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
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.