Alternatives to Procurement of Network Equipment

Procurement of Battery Energy Storage Systems (BESS) with Energy Management Systems (EMS) and Ancillary Equipment at Kyiv HPP / PSP, Kaniv, Kremenchuk and Seredniodniprovska Hydropower Plants, including related services (training of UHE staff)

Procurement of Educational Materials and Equipment for Classrooms and STEM Laboratories for Grades 8–9

Procurement of a Wave Generation and Attenuation System and a Frequency Converter for the Experimental Tank at the Admiral Makarov National University of Shipbuilding

Specialist in the Development of Dual Education and Education for Veterans

Specialist in International Cooperation and Internationalisation in Higher Education

Procurement of Agricultural Equipment for the Precision Farming Laboratory

Procurement of projectors for Odesa I.I. Mechnikov National University and Lviv Polytechnic National University

Physical and Learning Spaces Coordinator

Development of functionality for microqualifications

Procurement of an air conditioner for the server room at Odesa I.I. Mechnikov National University

Expected Outcome: Projects are expected to contribute to all the following expected outcomes: Effective and innovative methods for gathering, sharing and using large data sets in energy applications for the purpose of training AI models while ensuring privacy and security. Advanced and - wherever possible - open-source AI foundation models to support the digitalisation of the energy system, through improved grid observability, forecasting of supply and demand, advanced storage and renewables integration, demand side flexibility and energy efficiency. Enhanced cooperation, knowledge sharing and interoperability among energy system actors for secure and seamless data exchange. Advanced methodologies for AI model development ensuring that the results are FAIR (Findable, Accessible, Interoperable, Reusable) beyond the project ending, open source and accommodating for technology evolution. Scope: The quality of AI models depends strongly on the amount, quality, and representativeness of the data used for their training. Projects are expected to focus on developing, training and testing AI foundation models, using extensive data sets to accelerate the energy transition in key focus…

Expected Outcome: Projects under this topic will contribute to the following outcomes: Accelerate market expansion for European deep tech startups across EU regions and beyond by fostering collaboration and mutual support among European deep tech startup ecosystem actors; Interconnect top-tier tech startup and scaleup hubs embedded in research, technology and higher education ecosystems; Enable cross-border access to infrastructures, facilities, corporates, investors and talents to deep tech startups and scaleups, including those funded by the European Innovation Council (EIC); Enable more European deep tech startups, including women-led startups and scaleups, to succeed scaling into globally competitive companies; Strengthen less-connected innovation hubs by integrating them into a Europe-wide collaborative network; Boost private and public investment in deep tech ventures established within the participating innovation hubs. Scope: This action builds upon the EU’s strategic vision outlined in the EU Competitiveness Compass [1] , the EIC scaling strategy [2] , and the EU Startup and Scaleup Strategy [3] . It aligns with the objectives of strengthening Europe’s deep tech…