Five of the doctoral theses were defended at the School of Engineering, three at the School of Information Technologies, two at the School of Science, two at the School of Business and Governance and one at the Estonian Maritime Academy.

On 1 June, Kerli Onno, a doctoral student at the School of Business and Governance, defended her doctoral thesis Governing in Turbulence: Leveraging Experimentation and Data Capabilities for Building Adaptive Governance. Although data capabilities and experimentation can enable public sector organisations to become more dynamic and support their ability to adapt, interviews with public officials in Finland and Estonia showed that a number of factors influence their implementation and development.

On 9 June, Kristen Altof defended his doctoral thesis at the School of Engineering. His thesis, Development of a Pulsed Corona Discharge-Photocatalytic Oxidation System for Environmental Air Treatment, focused on developing a hybrid treatment system and evaluating its performance.

On 11 June, Riina Otsason defended her doctoral thesis at the Estonian Maritime Academy. Her research topic also attracted attention in both Estonian and international media. Her doctoral thesis, Environmental and Techno-Economic Assessment of Decarbonization Pathways for Ships Below 5,000 GT, aimed to assess the environmental impact, economic feasibility and operational constraints of reducing greenhouse gas (GHG) emissions in short-sea shipping, focusing on vessels below 5,000 GT.

On 16 June, Shangavi Subramanian, a doctoral student at the School of Engineering, defended her thesis Fabrication of In-Situ Alloyed Ti–Nb Alloys by Selective Laser Melting for Orthopaedic Applications. The research confirmed that the materials developed in the study had improved mechanical properties and stability as well as a better biological response, offering opportunities for designing next-generation implants.

On 18 June, Mayank Kumar Yadav from the School of Engineering defended his doctoral thesis Design and Development of Novel Partially Biodegradable Composite by Hybrid Manufacturing. While conventional metallic implants such as Ti64, stainless steel and Co-Cr alloys offer excellent mechanical strength but are non-biodegradable, biodegradable metals such as Mg, Zn and Fe offer controlled resorption and reduce long-term complications. However, their mechanical characteristics limit their use in load-bearing applications. In his research, Yadav explored ways to address these challenges by investigating Ti64 and Zn as potential candidates for developing next-generation partially biodegradable metallic systems using advanced manufacturing methods.

On 19 June, three doctoral theses were defended.

Janek Metsallik, a doctoral student at the School of Information Technologies, defended his thesis Interoperability and Governance in National Digital Health: A Framework-Based Argument for Integrative and Knowledge-Driven Approach. He studied interoperability and governance in national digital health, using Estonia’s national electronic health record infrastructure as the main empirical basis and complementing it with comparative observations from international digital health initiatives.

On the same day, Philipp Joram, a doctoral student at the School of Information Technologies, defended his doctoral thesis Datatypes with Symmetries in Homotopy Type Theory.

The day also marked a milestone for IT College, part of the School of Information Technologies, as Tiina Kasuk became its first doctoral graduate. Her thesis, Integrating Telepresence Robots into the Teaching and Learning Process, was also featured in the Estonian media.

After Midsummer Day, on 25 June, Qidi Jiang, a doctoral student at the School of Engineering, defended the doctoral thesis Robust Universal Framework for Measuring Sustainability on University Campuses Developing Towards Climate Neutrality (PICSOU). The author noted that the research was motivated by a critical gap between the reality of how university campuses operate as complex systems and the limitations of existing assessment tools, which often lack empirically grounded performance indicators directly linked to carbon reduction targets.

On 29 June, Tommaso Aguzzi, a doctoral student at the School of Business and Governance, defended his doctoral thesis Navigating Informality and Innovation: Entrepreneurial Adaptation and Strategies in Post-Soviet Kazakhstan and Kyrgyzstan. The research showed that semi-formal practices are widespread in these regions and that informal competition may, to a certain extent, even encourage innovation. Overall, the author concludes that semi-formality is an integral part of market life in post-Soviet Central Asia and that its relationship with innovation is conditional, multi-level and context-dependent.

On 30 June, two doctoral theses were defended.

Kadi Jakobson, a doctoral student at the School of Science, defended her doctoral thesis Physicochemical, Techno-Functional, and Sensory Properties of Plant Protein Food Ingredients and Their Technological Modification. Her research showed that both the choice of raw materials and targeted processing strategies can improve the techno-functional and sensory properties of plant proteins.

Marc Dolcet Sadurni, a doctoral student at the School of Engineering, defended his thesis Synthesis and Characterization of Sb1-xBixSeI for Optoelectronic Applications. The aim of the research was to develop and optimise synthesis methods for Sb1-xBixSeI (x = 0–1) and investigate how composition, structure and defects affect its optoelectronic properties, enabling its targeted use in solar cells and other optoelectronic devices.

One doctoral thesis was defended in July

On 16 July, Amirhossein Barzandeh, a doctoral student at the School of Science, defended his doctoral thesis Machine Learning Analysis of Baltic Sea Surface Current Variability. The main aim of the research was to demonstrate that the complex variability of surface currents in the Baltic Sea can be described and predicted by combining the outputs of physics-based models with data-driven machine learning methods.