Open calls in doctoral studies, third period
The third application period took place from 15 to 30 September 2024.
Starting from the 2022/2023 academic year in most cases the admitted doctoral student will work as junior research fellow at the university. In some cases, admission may be for a study place without a work contract. Read more about status and funding of doctoral students.
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.
Interview
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. 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.
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: Leho Tedersoo, Saad Alkahtani (King Saud University)
This project aims to disentangle the biotic and abiotic factors driving desert microbiome, using state-of-the-art molecular, bioinformatics and statistical methods. The purpose is also to understand how planting of semidesert trees may benefit from the local and inoculated microbiome. In all, the project seeks for ways how to ameliorate climate and land use change impacts on desert ecosystems.
Computer Science
Supervisor(s): Kallol Roy, Madis Kiisk
Cosmic ray tomography (CRT) uses cosmic-ray muons for 3D imaging to detect hidden objects. CRT has transformative potential in areas such as airport security, infrastructure inspection, and healthcare. By analyzing Coulomb scattering of muons, it reconstructs and differentiates materials. However, more research is needed for CRT to be a practical alternative to X-ray technology, as it faces challenges like scanning time, tracking accuracy, momentum estimation, and cost efficiency. An AI solution powered by deep learning (DL) is proposed to enhance tomographic image reconstruction and material classification for CRT. The DL model, trained on muon tracking data, addresses reconstruction and classification tasks. Using the Spectral Gap technique, it detects narcotics and nuclear materials by analyzing muon ray reception after scattering. Spectral gap is a powerful technique, and it disentangles the variable and correlated optimal muon rays from others in the time-integrated muon energy spectrum. This novel method employs raw muon ray data for inverse modeling, representing a pioneering approach in the field. The base machine learning model can be used to solve the inverse problem of image reconstruction in other domains e.g. Terahertz Sensing.
Supervisor(s): Marek Oja, Kerli Mooses
Clinical guidelines (CG) and care pathways (CP) aim to enhance the quality of care, increasing patient safety and ensure the effective use of healthcare resources. The real-world data from healthcare provision claims, electronic health records and e-prescriptions can provide more thorough understanding about the actual situation as well as trends over time providing an invaluable input both to the development and evaluation of implementation of the CG and CP. This in turn will help to improve the quality of healthcare services.
The current doctoral project is aimed at improving the secondary use of Estonian health data in the development, monitoring and evaluation of CG and CP. The focus of the PhD project will be on the development of standardised reusable solutions (e.g. R package) to easily extract and visualise the information for CG and CP development and evaluation together with identifying deviations from CG or CP costs to the healthcare system. This project will utilise a database containing all health events from EHR, healthcare provision claims and e-prescriptions for a representative sample of Estonian population and which has been transferred to OMOP common data model (CDM). This in turn ensures the applicability of created solution in international scale.
Chemistry
Supervisor(s): Siim Salmar, Anu Ploom
Lignocellulosic biomass, including wood, as a renewable resource has great potential to defossilize the carbon-based chemicals and materials industry. Therefore, modern biorefining technologies aim on conversion of low-quality wood and wood processing residues into high value biochemicals and materials. These approaches involve decomposition of wood starts by converting hemicellulose into water soluble monosaccharides and insoluble mixture of lignin and cellulose (LCM, lignin-cellulose mixture). The further focus of these approaches is obtaining free sugars, cellulose derivatives (microcrystalline cellulose and nanocellulose materials) and pure lignin form LCM.
In this project, in cooperation with the wood biorefining company Fibenol OÜ, we investigate for the first time the possibilities of direct valorization of LCM into high-value materials. We use solvent fractionation to control the content of lignin and cellulose in LCM and use ultrasound technology to prepare novel lignin-nanocellulose (nLCM) materials. Principles of these approaches were developed in our laboratory within the framework of our previous fundamental studies. As a part of the project, ultrasonic treatment conditions will be optimized to obtain novel nLCM materials on a pilot scale, and applicability of these materials for polymer synthesis and prototype material fabrication will be studied.