Projects

The Engineering Academy is a project initiated by the Ministry of Education and Research and funded by the European Social Fund, with the goal of improving the quality of engineering education and reducing the labor shortage in technical fields. The project is led by the Education and Youth Board and is joined by five higher education institutions. The Engineering Academy includes 22 engineering-related study programs, of which ten have been selected as priority focus programs for development. The project has three focus areas: • Increasing the number of applicants • Improving the quality of education and Increasing alignment with labor market needs • Reducing dropout rates The Technical University has set a goal to increase admissions in the field of engineering by 15% each year. To improve the quality of education, the action plan includes a significant expansion of project-based and problem-based learning, curriculum development, quality enhancement, and infrastructure upgrades. Additionally, lecturers’ training and the recruitment of teaching assistants are planned. To reduce dropout rates, individual support for students will be increased, both during the first year and when completing their final theses. First-year students will also be offered additional mathematics courses. The goal is to significantly reduce dropout rates and increase the number of graduates.

The project aims to advance Electric Propulsion Drive System (EPDS) Digital Twin (DT) technology for Software Defined Electric Vehicles (SDEVs), with a focus on achieving DT adaptive and intelligent levels. It addresses the need for efficient testing and evaluation of electric propulsion systems in line with EU clean energy transition goals. Leveraging the rapid development of DT technology, the project seeks to contribute to SDV technology through enhanced modeling, data gathering, IoT integration, and system optimization. Key challenges include lifecycle management, data processing, and real-time communication between physical and virtual systems. The project encompasses advanced modeling, data gathering, IoT, and communication infrastructure, system integration, optimization, and technology demonstration.

This multidisciplinary study aims to decrease cardiovascular mortality in Estonia by increasing treatment adherence and empowering patients to create a supportive self-management environment for monitoring their health and actively participating in the treatment process. Analysing 1) the LDL-cholesterol values of North Estonia Medical Centre (NEMC) patients to find underdiagnosed and undertreated patients and 2) treatment adherence to lipid-lowering drugs (LLD). Identifying patient groups who need additional support. During the pilot project, a novel application will be developed, together with personal support, used to increase LLD adherence. The novelty of the tool – combining the data used in Estonia from the Nationwide Health Information System, ePrescription, and NEMC electronic medical record with the data collected by the patient and enabling two-way communication between the patient and medical staff. In the last stage of the study, an impact assessment of the tool is planned.

Life-sustaining artificial kidney treatment or haemodialysis (HD) is needed for end-stage renal disease and critical care patients. High quality HD must ensure effective and personalized blood purification from harmful uremic toxins (UT), among inflammation-cardiovascular disease related middle size (MM)-UT. This, urged by a need for HD surveillance in crisis (coronavirus, war, energy), has created a demand for on-line, bloodless, and non-infectious tools for UT removal monitoring. Optical monitoring can provide a feasible tool for this. However, to date no reliable monitoring technology of MM-UTs is available. This project aims to fill this knowledge gap. Optical spectral signature identification combined with chromatographic and biochemical analyses to reveal the main optical MM markers in biofluids, selection of best signal processing algorithms, and a proof-of-concept in-vivo clinical study is planned to develop a novel optical technology for on-line MM-UT removal monitoring in HD.

EML brings together the high-level infrastructure of understanding and application of magnetism and electromagnetism of four partners - University of Tartu, Tallinn University of Technology, Institute of Chemical and Biological Physics, and Metrosert with the aim of enabling extensive use of this expertise and knowledge by companies and the public sector, also in order to achieve TAIE and Estonia 2035 goals. EML partners with NEO materials (Sillamäe) and magnet (Narva) factories. EML includes three distributed core laboratories - Magnets and magnetic materials, Recycling of rare elements, and Electro-magnetism applications - bringing together the versatile know-how of EMLpartners. The laboratories provide top-level electromagnetic analysis expertise, develop methodologies, offer scientific and industrial magnetic analyzes, and provide professional training. EML represents Estonia in international organizations (EMA, EMFL, NHMFL, COST) and participates in numerous international projects.

DigiBio project focuses on digitalisation, bioeconomy, and sustainability, scientific domains which constitute a high priority in national, regional, and EU strategies and policies. As the second large European Centre within this area, the Estonian Centre for Bioesustainability (ECB) will place Estonia in a very competitive position in European R&I. With DTU assistance, ECB will establish a major research, technology development, and innovation platform for the generation of cutting-edge bioengineering solutions focused on sustainable bio-production through biology digitalisation. This platform will accelerate lab-to-market translation of bioengineering solutions, diversifying Estonian national industry. DigiBio’s overarching objective is to establish a state-of-the-art CoE for digitalisation of biology in Estonia, through upgrading the ECB.

CERN is an international scientific centre that unites 24 member states and 10 associated states. Their mission is to conduct experiments in high and low energy particle physics and develop novel technologies and IT solutions for medicine, AI and quantum computing. Since 2023, Estonian research at CERN has been coordinated by the CERN consortium formed by the National Institute of Chemical Physics and Biophysics (KBFI), the University of Tartu (TÜ) and Tallinn University of Technology (TalTech) whose members participate in LHC CMS, WLCG, FCC, CLIC, iFast, AMBER, Cloud and CCC experiments. Cutting-edge research at CERN fosters internationalisation and advancement of Estonian science, technology and IT. CERN actively trains students, doctoral candidates, teachers and engineers, contributing to the next generation of Estonian scientists. This application presents the operational and infrastructure plan for CERN scientific infrastructure and consortium until the end of 2029.

The challenge in modern industry is to find the best ways for human-robot interaction in workplaces, enabling robots to realize optimal solutions by combining AI and human capabilities. The project's goal is to contribute to the automation of company production processes, focusing on the social and psychological aspects of human-robot collaboration to ensure that human workers in the industry feel safe and satisfied. Research directions include: - creating a collaborative robotics experimental lab - designing robotized workplaces - modelling of human-robot interaction, assessments and analysis of influencing factors and risks. Expected results are methodologies and validated human-robot interaction models, skills for their implementation, impact factors and risk assessments, a developed laboratory with hardware, software and expertise; providing user-centred design solutions services. All of this leads to safer human-robot interactions, increasing user trust in robotic systems.

The present project aims to develop novel and enhance existing methods of explainable artificial intelligence for the analysis of human motor functions. Pilot studies have demonstrated promising results to support the diagnosis of neurodegenerative diseases. In addition, we plan to extend the area of application from medicine to cognitive development and cognitive fatigue analysis. The integration of the explainer component will provide medical professionals with the necessary transparency of the decisions made by AI. Application in the area of cognitive development to support the school education process. Cognitive fatigue is known to cause severe injuries and serious financial losses. In-depth understanding of this phenomenon and ability to recognise mental fatigue targets to make the work environment safer and reduce monetary and non-monetary losses in the process of work.