REPLICATE

HORIZON-WIDERA-2026-06-ERA-08

REPLICATE will strengthen the reliability, transparency, and societal value of European research by making replication a more routine and well-supported practice across diverse research contexts. The project conducts coordinated direct replications of high-impact studies across multiple research domains, including fields where replication remains methodologically or epistemically underdeveloped. The University of Tartu contributes expertise in AI governance research, including a cross-national examination of AI governance priorities in national AI strategies. Beyond conducting replication studies, REPLICATE develops methodologies, tools, and frameworks to support replication and works with researchers, funders, publishers, research organisations, and research infrastructures to strengthen incentives and practices for reproducible and trustworthy science. The project will generate open data and protocols, new knowledge, and a sustainable European community of practice aligned with Open Science and responsible research.

Duration: 2027-2030. Grant amount: 2,499,999.75€

INSPIRE – Innovative Networks for Smart Policies, Integration and Research on Digital Transformation and Governance in Smart Sustainable Cities

HORIZON-MSCA-2026-SE-01-01

INSPIRE develops and validates a sociotechnically co-produced Platform Governance Framework for Smart Sustainable Cities, recognising that successful digital transformation depends not only on technology but also on institutional, social, and contextual conditions. The project brings together 39 partners across Europe and beyond and works across paired city contexts to connect research, governance, and practical experimentation to address urban governance challenges including climate resilience, digital inclusion, and urban transformation.

Grant amount: € 803,050.00

AI-GUIDE – AI-Governance, Use, and Impact for a Dynamic European R&I Ecosystem

COST Action CA25157

AI-GUIDE addresses Europe’s urgent need for coordinated, responsible and inclusive integration of generative artificial intelligence (GenAI) across the research and innovation (R&I) ecosystem. The Action responds to fragmented policies, uneven adoption, and widening capacity gaps among research institutions. It brings together experts from higher education, research-performing organisations, funders, publishers, policymakers, SMEs and professional associations to co-create shared governance, operational guidelines and practical tools for trustworthy GenAI use in research. By connecting scientific, technological, research management, ethical and policy expertise, AI-GUIDE will strengthen Europe’s global leadership in responsible AI for science.

ENDUeAI – European Network for Developing and Evaluating the Use of GenAI in Education

COST Action CA25177

ENDUeAI is a pan-European interdisciplinary research network that aims to establish a coordinated, interdisciplinary, and pan-European research network examining the governance, use, and impacts of AI across research and innovation ecosystems, with a focus on evidence-based, responsible, and inclusive approaches to AI adoption. Through interdisciplinary collaborations, we will co-create: a validated GenAI evaluation framework, privacy-compliant middleware standards, explainability benchmarks, inclusive multimodal labs, evidence-based pedagogy and literacy modules, policy-ready ethics & privacy guidelines, and a Horizon Europe funding proposal for long-term sustainability.

SHAPE – Smart City Higher Education Advancement Program in East Africa

ERASMUS-EDU-2025-CBHE-STRAND-2

SHAPE strengthens higher-education capacity for context-sensitive smart city development in Kenya, Somalia, and Uganda. The project develops new curricula and training around AI and open data for smart cities, smart health, smart energy and other sectors, while connecting universities across Europe and East Africa and strengthening links with regional innovation ecosystems.

Grant amount: € 719,987.00

RSC Compass – Responsible Supply Chain Strategy Alignment Tool

TEM-TA259 | Nationally funded R&D project (co-funded by EU and Ministry of Education and Research)

RSC Compass develops an AI-supported analytical framework and decision-support tool for improving the alignment between responsible supply chain strategies, business processes, operational performance indicators, and enabling digital technologies. The project addresses a persistent challenge in sustainable supply chain management of translating high-level environmental, social, and operational ambitions into measurable objectives, aligned processes, and informed technology decisions. Building on established supply chain performance management approaches, including the SCOR framework, and an existing catalogue of digital technologies and use cases, the project will develop an operational strategy-alignment methodology. This will connect organisational strategy statements with relevant business processes and performance indicators, assess the degree of alignment between strategic priorities and operational measurement systems, and identify opportunities for process improvement and technology adoption. The resulting RSC Compass prototype will visualise strategy performance alignment and support organisations in identifying gaps, prioritising improvements, and exploring suitable digital technologies based on organisational context, supply chain maturity, and evidence from existing technology use cases. The project combines conceptual development, expert validation, empirical case studies, and prototype development, with a particular focus on responsible and sustainable supply chains and logistics. Its expected contribution is both methodological and practical: a validated approach for connecting strategy, processes, metrics, and technology choices, and a tool to support more evidence-based and actionable supply chain transformation.

Grant amount: € 810 000.00

ECOAI – Empowering Conservation with AI in Asia

ERASMUS-EDU-2026-CBHE-REGION-5a

ECOAI builds capacity for responsible and sustainable AI use in environmental conservation across Bangladesh, Bhutan, and Laos – three climate-vulnerable countries where less than 5% of conservation professionals possess AI skills. The project combines curriculum development, micro-credentials, academic staff training, innovation labs, and an EU–Asia Centre of Excellence for Green AI to strengthen institutional and human capacity for AI-enabled conservation.

Grant amount: € 777,679.00

NOESIS – Network for Oceanic Sensing and Integrated Science

HORIZON-MSCA DN

NOESIS addresses the challenge associated with the European Blue Economy facing a critical double deficit of a shortage of engineers who understand the ecological context of the ocean, and a lack of biologists who possess the digital fluency to handle massive, multi-modal datasets by training a new generation research architects who are capable of co-designing the entire observation pipeline from the energy budget of a deep-sea sensor to the policy implications of biodiversity data. NOESIS achieves this by replacing the traditional linear research model with an integrated discovery cycle structured across three functional clusters. Technological Foundation develops resource-efficient, embedded hardware (TinyML, Submersible Micro Data Centers, bio-friendly acoustics) to overcome the physical constraints of power and bandwidth in the deep ocean. Methodological interface creates physics-informed AI and domain-generalization algorithms to bridge the gap between raw sensor data and meaningful environmental variables, solving the crisis of model transferability. Scientific Frontier applies these tools to generate predictive risk models for biodiversity, fusing eDNA, acoustics, and optical data to operationalize Dynamic Ocean Management.
Through a unique dual-axis secondment strategy, every Doctoral Candidate gains deep disciplinary expertise while validating their tools in a contrasting scientific culture (e.g. engineers deploying hardware at biological stations). By integrating PLOCAN and CNR as associated partners, NOESIS links its technological innovations with strong ocean observation expertise and infrastructure. This allows NOESIS to transition ocean observation from passive, retrospective logging to real-time, autonomous Ocean Intelligence, securing Europe’s leadership in the Digital Ocean.

Duration: 2026-2030. Grant amount: € 4 487 762,16€

EIT Urban Mobility Master School

HORIZON

EIT Urban Mobility is a Knowledge and Innovation Community (KIC) launched by the European Institute of Innovation and Technology (EIT) to rethink and improve the use of urban spaces and ensuring safe, efficient and sustainable multimodal mobility. Together with its Partner Universities, EIT Urban Mobility has established a Master School to educate future entrepreneurs and innovators in the field of urban mobility. The Urban Mobility Master School is part of the educational activities carried out by EIT Urban Mobility in its goal to educate future entrepreneurs and innovators for Europe. Together with its Partner universities, EIT Urban Mobility is striving to establish the Master School as frontrunner for innovation and entrepreneurship in rethinking urban spaces and mobility for a prosperous Europe. The EIT Urban Mobility Master programmes are designed to conform to the EIT Label, a certificate of quality that is awarded to excellent educational programmes that focus on innovation, entrepreneurship, creativity, and leadership and that are based on the integration of education, research and business.

Duration: 2020-2025. Grant amount: 567 533,36 EUR€

DECIDE – Development of a dynamic informed consent system for biobank and citizen science data management, quality control and integration
The scientific objective of the project is to develop dynamic consenting and survey system prototype to integrate academic research conducted in biobank with citizen science initiatives maintaining data credibility via hierarchical metadata harmonization and assessing ethical, legal and social implications (ELSI).

DECIDE is funded by the European Regional Development Fund (ERDF), Measure 1.1.1.1 “Support for applied research”

HORIZON2020 INTEGROMED “Integration of knowledge and biobank resources in comprehensive translational approach for personalized prevention and treatment of metabolic disorders”
INTEGROMED is aiming to improve and optimize the use of resources provided by large biobanks and prospective cohorts in combination with national health care system, improve knowledge in establishment of clinical studies for development of precision medicine approaches. The strategy of INTEGROMED is to increase excellence and develop sustainable approach targeting virtually all aspects of translational medicine by integrating the experience of research excellence institutions and existing resources by creating a research and innovation network with three internationally leading organizations in the translational medicine area – University of Dundee, Lund University, and Weizmann Institute of Science.

INTEGROMED is funded by the European Union under the Horizon 2020 programme, grant agreement 857572.

Optimizing e-mobility solutions using artificial intelligence (AI)
Position: volunteer/ researcher
The research has received funding from the research project “Competence Centre of Information and Communication Technologies” of EU Structural funds, Research No. 1.15 “The use of business process models for full functional testing of information systems”.

The use of business process models for full functional testing of IS
The proposed data quality model-based testing methodology (DQMBT) supposes creation of a description of the data to be processed by IS and the data quality requirements used for the development of the tests, followed by performing an automated test on the generated tests verifying the correctness of data to be entered and stored in the database.
The research has received funding from the research project “Competence Centre of Information and Communication Technologies” of EU Structural funds, Research No. 1.7 “The use of business process models for full functional testing of information systems”. European Regional Development Fund (ERDF) programs “Growth and employment” Specific Support Objective 1.2.1 “Increase private sector investment in R&D” Measure 1.2.1.2 “Support for the improvement of the technology transfer system”. Agreement with the Latvian Investment and Development Agency (LIAA) on participation in the technology transfer.

Concurrence analysis in business process models
The aim is to propose a technology and methodology to detect and prevent the possibility of incorrect execution of concurrent business processes. Analyzing business process according to the proposed procedure allows to configure transaction processing optimally.
The research has received funding from the research project “Competence Centre of Information and Communication Technologies” of EU Structural funds, Research No. 1.6 “Concurrence analysis in business process models”. European Regional Development Fund (ERDF) programs “Growth and employment” Specific Support Objective 1.2.1 “Increase private sector investment in R&D” Measure 1.2.1.2 “Support for the improvement of the technology transfer system”. Agreement with the Latvian Investment and Development Agency (LIAA) on participation in the technology transfer.

Data Quality Management by using Executable Business Process Models
The study proposes a data object-driven approach to data quality evaluation. This user-oriented solution is based on 3 main components: data object, data quality specification and the process of data quality measuring. These components are defined by 3 graphical DSLs. The approach ensures data quality analysis depending on the use-case & allows analysing quality of “third-party” data. The proposed solution is applied to open data sets.
Position: volunteer/ scientific assistant
The research has received funding from the research project “Competence Centre of Information and Communication Technologies” of EU Structural funds. Research No. 1.8 “Data Quality Management by using Executable Business Process Models”. European Regional Development Fund (ERDF) programs “Growth and employment” Specific Support Objective 1.2.1 “Increase private sector investment in R&D” Measure 1.2.1.2 “Support for the improvement of the technology transfer system”. Agreement with the Latvian Investment and Development Agency (LIAA) on participation in the technology transfer.