All faculties presented project proposals to the competition for AI-related doctoral positions

Twelve interdisciplinary doctoral positions will be created at the University of Tartu to explore potential applications of artificial intelligence, including, for example, addressing mental health issues, reducing food waste, assessing the overall impact of spatial planning, and facilitating sign language interpreting.

The university’s internal competition for the supervisors’ project proposals received 54 applications, the majority (43) from the Faculty of Science and Technology. Five proposals came from the Faculty of Social Sciences, four from the Faculty of Medicine, and two from the Faculty of Arts and Humanities.

These state budget-funded doctoral positions, allocated to the university under the new administrative contract, are related to interdisciplinary research or the application of artificial intelligence and correspond to anticipated labour market needs outside the higher education and research sector.

Professor Mari Moora, Vice Rector for Research at the University of Tartu and chair of the evaluation committee, said she was pleased that research problems to be solved through doctoral research were proposed to the competition by all faculties. Although most of the shortlisted projects came from the Faculty of Science and Technology, many are connected to other fields. Proposals were submitted by the Institute of Computer Science, the Institute of Chemistry, the Institute of Physics, the Institute of Ecology and Earth Sciences, the Institute of Psychology, and the Institute of Technology, as well as by Tartu Observatory. In the Faculty of Arts and Humanities, the Institute of Estonian and General Linguistics received one doctoral position.

Competition to fill the doctoral positions begins in the autumn

The applications submitted to the competition were evaluated by a 13‑member committee comprising representatives of the university’s four faculties and four external evaluators.

The committee evaluated each doctoral project’s compliance with the competition requirements, the research group’s capacity to support the doctoral researcher’s progress and research, the availability of necessary resources and the effectiveness of previous supervision.

Applicants interested in the selected projects are welcome to apply to the public competition during the autumn admission period. If no suitable doctoral applicants are found, the position is offered to the next-ranking project, and a new public competition is announced in the next admission period.

Under the administrative contract, similar competitions for AI-related doctoral positions will also be held in the next two years.

Doctoral projects open to applicants in the autumn:

• “AI-Assisted Research Memory and Knowledge-Driven Experimentation in Electrochemistry”, supervisor Nadežda Kongi
• “Joint self-supervised learning of terrain geometry and traversability using passive and proprioceptive sensors in non-structured environments”, supervisors Mihkel Pajusalu and Aditya Savio Paul
• “Adaptive NMR methodologies for integrated structure elucidation in bio and pharma fields”, supervisors Ivo Leito, Stefan Kuhn and Lauri Toom
• “Linguistic annotation with LLMs”, supervisor Maciej Eder
• “Architectures and evaluation methods for structured LLM-mediated psychological interventions”, supervisors Kairit Sirts and Helen Uusberg
• “AI-based forecasting of socio-spatial urban change: applying explainable AI and spatial machine learning to assess planning impacts”, supervisors Anneli Kährik, Tiit Tammaru and Anto Aasa
• “AI-driven low-cost pervasive sensing for sustainable agriculture supply chains”, supervisors Huber Flores, Zhigang Yin and Margit Kõiv-Vainik
• “Integrating artificial intelligence for efficient cross-species processing and modeling of large-scale animal telemetry data in applied contexts”, supervisors Tuul Sepp, Raul Vicente, Mart Jüssi and Richard Meitern
• “Mapping atmospheric autooxidation mechanisms of climatically relevant low-volatility compound formation via multi-reagent mass spectrometry and AI-driven modeling”, supervisors Heikki Junninen and Pilleriin Peets
• “AI-based task planning for human-robot teams in unstructured environments”, supervisors Karl Kruusamäe and Robert Valner
• “Human–AI collaboration models in workplace learning for data engineers: development and empirical validation of a typology”, supervisor Marina Lepp
• “Multilingual sign language modelling in under-resourced conditions”, supervisors Mark Fišel and Kalev Koppel