Open calls in doctoral studies, third period

The third application period will take place from 15 to 30 September 2022.

Doctoral students admitted to these projects will have employment contracts with the junior researcher. The expected workload is 1.0, the expected work period is four years.  The final workload will be set during the negotiations.

Admission conditions and evaluation criteria of the faculty

In the Faculty of Science and Technology, all candidates must submit a motivation letter and a CV in the Dreamapply together with the application. Candidates will be assessed on the basis of a motivation letter and an entrance interview. (except for the science education, where a draft for doctoral project must be submitted instead of a motivation letter). Candidates will apply for announced projects.

Motivation letter

Please write a brief motivation letter (in English, maximum of 6000 characters with spaces) based on the following points:

1. Why are you interested in this PhD project, explain your choice.
2. What is your previous experience in this field? Explain how your educational and professional background relates to the project you are applying to.
3. What are the analytical/scientific methods you have practiced.
4. Describe briefly the methods and main results of your MSc thesis.
5. Decribe your earlier research activities, including research publications and conference presentations, if available.
Assessment criteria for motivation letter:

- motivation and argumentation of skills and the choice of the project
- relevant study and work experience and other relevant activities (publications, project management etc.) as required to present in the motivation letter.


The applicant must describe the wider scientific background of the doctoral project and possible applicability of the results, also their motivation to be admitted to PhD studies with particular project. The entrance interview conducted by the committee lasts up to 25 minutes. Only applicants whose motivation letter is assessed positively will be invited to the interview (minimum positive result is 35 points out of 50).

The entrance interview is used to assess the following:

- knowledge of the wider scientific background of the project and possible application of the expected results
- applicant’s motivation to pursue doctoral studies in the relevant field of science and to work in this field
- wider analytical and generalization skills regarding the research and study topics.
The entrance interview takes place most probably in June 2022.
International applicants who cannot be present at the interview in Tartu, may conduct an online interview. Applicants will be informed of their interview date and time by the respective faculty.

Both the motivation letter and entrance interview are assessed on a scale of 0 to 50 points, minimum positive score is at least 35 points. To be invited to an interview, the applicant must earn at least 35 points for the motivation letter.

Projects for application:

Biodiversity and Ecological Sustainability

Supervisors: Marina Semtsenko, Mari Moora

Many ecosystems exhibit alternative stable states, such as vegetation dominated by woody or herbaceous species, that cannot be explained by climatic factors. The probability of a location being dominated by woody or herbaceous vegetation may be driven by local perturbations such as herbivory or fire. Evidence is accumulating that soil microbial communities could play a major role in structuring plant communities. However, their role in driving transitions between alternative stable vegetation states remains poorly understood. This PhD project aims at disentangling the microbial drivers of shifts between grassland and forest vegetation and the role of microbial communities in stabilising grassland vegetation in the face of external perturbations, such as an increasing frequency of severe droughts driven by climate change. The project will focus on different types of mutualistic associations between plants and mycorrhizal fungi as key drivers of transition between grassland and forest. In addition, the project will investigate the consequences of soil biodiversity loss in agricultural landscapes, focussing on the role of mutualist limitation in grassland restoration and resilience to drought events. Collectively, this project will advance our understanding of the roles microbial communities play in maintaining vegetation at a stable state and will provide recommendations for effective habitat restoration via microbial manipulation.


Supervisor: Vijayakumar Anand

Infrared microspectroscopy (IRM) is a powerful analytical tool for molecular characterization of functional groups and their spatial information in materials. Traditional IR microspectroscopic instruments comprise a conventional dual beam optical microscope and a table-top Fourier Transform Infrared (FTIR) spectrometer. This PhD project entitled “Holographic infrared fingerprinting technology” aims to develop a new generation of IR spatio-spectral imagers using diffractive optical elements and meta optical elements fabricated on IR-transmitting substrates (such as calcium fluoride and barium fluoride). The fabrication will be carried out using lithography procedures. The characteristic absorbance spectrum of samples of interest will be mapped using a synchrotron-IR microspectroscopic technique, to obtain unique spatial signatures at an enhanced signal-to-noise ratio. Subsequently, a programming system based on Matlab software will be developed and trained to discriminate spatial information at different wavenumbers prior to forming a 3D holographic image. A high-efficiency non-linear crystal will be used to upconvert IR to visible, enabling the application of a high-resolution visible sensor. Furthermore, polarization multiplexing techniques using the synchrotron-IR light source will be applied to study birefringence and anisotropy of materials at a high spatial resolution. 

Supervisor: Vijayakumar Anand

Imaging objects through a scattering medium is always a challenging task. In recent years, there has been significant growth in this research area due to the developments in computational imaging technologies. Invasive and non-invasive approaches were developed to extract useful information about the object from the transformed data. However, the above methods are successful only for simple objects. The doctoral project titled "Development of computational imaging techniques for imaging through scattering layers using structured light" aims to approach this problem by employing structured light. Certain structured optical beams have shown a high resilience to scattering, and, in this project, such beams will be employed as carrier beams to carry object information through the scattering media and deliver it to the recording device. Novel reconstruction methods will be developed to reconstruct the object information from the recorded distorted signals. Unlike the previous methods, which relied only on the computational optical methods, the proposed direction addresses the problem on two fronts: employing resilient scattering beams and computational optical methods. The project's outcome will be a better technology to see through scattering layers such as fog, biological tissues, and turbid media.

Computer Science

Supervisor: Radwa El Shawi

The goal of this doctoral work is to introduce an interactive and explainable automated unsupervised framework for data clustering. Nowadays anyone who tries to cluster data, whether a data-mining expert or a common user, is faced with an unclear decision over which algorithm to use and how to set the cluster hyper-parameters. It is extremely difficult for users to decide which algorithm would be the best choice for a given set of data [1]. Currently, many data scientists choose a particular algorithm rather for its speed or thanks to their previous experience with that algorithm on a completely different problem. Cluster analysis is typically employed in the initial phase of exploring raw data, where prior knowledge is minimal. Having automated methods is crucial, especially in the modern era of “big data” where manual data investigation would be overwhelming. The framework should empower its users to develop their own satisfying and trusted models. It should reduce the burden on for data scientists and domain experts for going through the time-consuming process of building and deploying scalable machine learning models.

Supervisor: Dmytro Fishman

Dependance on large amounts of labeled data is one of the greatest liabilities of today’s deep learning approaches that otherwise have impressed on a great number of occasions. Labeling data is a time-consuming, expensive, and error-prone process. This is especially relevant for the field of biomedical image analysis, which is famous for its extremely heterogeneous data, and overworked experts. At the same time, multiple methods have been recently proposed that efficiently make use of only superficially labeled or completely unlabeled data. Therefore the focus and the goal of this PhD project will be exploring, applying, improving, and developing new state-of-the-art approaches that need no or very few annotations for biomedical image analysis. This work will be pivotal for the further development of the deep learning methods in the biomedical domain, contributing to their faster adoption and application.

Supervisor: Ulrich Norbisrath

This thesis analyzes a network of IoT devices, with state-of-the-art architecture and resource allocation mechanisms, in terms of key performance parameters, i.e., latency, security and energy and spectral efficiency. The coexistence of large number of devices within the same network poses threats in terms of efficient resource utilization to meet capacity requirements and latency issues while providing reliable communication. The thesis devises an optimized low-latency, energy-efficient green framework. Security of information flow in said network is analyzed and optimized using IoT based micro-transactions using smart contracts. The study ensures that the IoT devices can share data securely across various stakeholders, minimizes the possibility of cyberattacks on IoT networks, optimizes sustainability of network, makes the registration and validation process of new devices adding to the network autonomous. Thus, the overall health of the IoT network improves significantly.

The student builds and develops the proposed solution on top of existing work carried out by researchers. The important steps in this regard are: Extensive literature review of IoT network architectures and most suitable resource allocation techniques and IoT security techniques. The expected outcome of this step would be a detailed comparative analysis of her proposed solution with the existing work. Further, framework optimization would be carried out based on this analysis and verified with local and international industrial partners.

Supervisor: Rajesh Sharma

The hate speech being spread on online social media (OSM) platforms often impacts offline events, such as the 2016 US elections or creating insecurities among minorities which affects the social fabric of the society. Government organizations and policymakers have enacted various laws to control the spread of hate speech. In this PhD project, we plan to study the spreading of hate speech on online social media platforms through multiple approaches. In our first approach, we will perform data curation and empirical analysis on data collected from various social media platforms (Twitter, YouTube, Reddit, etc.), to study different kinds of hate speech such as i) xenophobic, ii) religious-based (anti-Semitism), iii) gender (misogyny), and iv) sexual minorities (LGBT+). Furthermore, we would employ AI models for predicting the spread of hate speech. As most of the state of the art AI approaches are black-box in nature, thus, to overcome, we plan to propose Explainable AI models for the detection of hate speech. In our second approach, we will focus on modeling the spreading of hate speech using epidemic modeling to cover the geo-lingua landscape by tuning various parameters of theoretical models. To summarize, by applying various AI, NLP, and network science approaches, we plan to curb the menace of hate speech on online social media platforms.

Supervisor: Rajesh Sharma

As humans are social beings, we love to socialize with others. With the advancement of technology, we have found easier ways of social interaction through online social media (OSM). There are different types of OSM platforms such as Reddit, Twitter, Facebook, etc. These OSM platforms have also provided a way of expressing or seeking support for various needs, including personal, and mental health issues is one of the prominent ones. With the fast-moving lifestyle in recent times, various mental health issues are on the rise among individuals. In this Ph.D. project, we will explore various OSM platforms to collect data for analyzing mental health issues. We also plan to explore existing datasets on mental health issues. We are going to focus specifically on groups of individuals more vulnerable to mental health issues such as Childhood Sexual Abuse (CSA) victims, LGBTQ, and also mental health issues related to various health conditions such as Sleep Apnea, Cancer. We will follow a multimodal approach to analyze mental health issues. Multimodal means the data for analysis includes more than one modality. That is text, audio, image, or video. We plan to understand different emotions using multimodal approaches as understanding emotions can help us understand the associated mental health issues. We also plan to explore different explainable AI approaches as it is an important aspect when dealing with a sensitive domain such as mental health.

Supervisor:  Raul Vicente Zafra

Grid cells in the entorhinal cortex exhibit a striking pattern of activation. They fire at multiple places arranged on a hexagonal lattice of two-dimensional open areas. They are thought to be key for planning and memory in both spatial conceptual spaces. Mathematically, computational theories have recently identified grid cell patterns as eigenvectors of matrices encoding the structure of the environment. The main objective of this project is to solve the direct and inverse problems for the relation between environments and grid cells to test theories of spatial and abstract navigation. To this end, we propose to formalise the inverse problem as a constrained optimisation problem, and applying it to experiments in humans and rodents to elucidate the computational role of grid cells.

Space research and technology

Supervisors: Andris Slavinskis and Pekka Janhunen

Slavinskis and Janhunen have cooperated in electric sail demonstration since the ESTCube-1 project in 2011. They have been publishing ESTCube-1 mission design and results, ESTCube-2 mission design, FORESAIL-1 design, Aalto-1 design and results, and the MAT concept and design together. With the first two ESTCubes, the goal has been to demonstrate the E-sail in low Earth orbit (LEO) where it can also be used as a plasma brake for deorbiting. With ESTCube-3 and this project, we will close the gap between the LEO demonstration results and the future applications of the E-sail, such as MAT. This project will design the ESTCube-3 mission to demonstrate the electric sail in its authentic environment, the solar wind. Palos will improve and publish E-sail mission design tools and propose a secondary mission objective to flyby a near Earth asteroid.


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