CoolbrAIn is a member of EIC Effitronics Portfolio
EIC Effitronics Portfolio
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The EIC Pathfinder Portfolio “In Space Solar Energy Harvesting,” funded by the European Innovation Council (EIC), is a visionary initiative uniting nine cutting-edge projects to revolutionize how solar energy is collected, transmitted, and used in space.
The projects selected are from the space challenge call WP2023 In Space Solar Energy Harvesting topic defined and developed under the Program Manager for Space Systems & Technologies, Stela Tkatchova. In space solar energy harvesting (WP2023 Pathfinder) call (TRL1-4) is about advancing concepts, methods and technologies for new types of antennas, rectennas, solar energy conversion and microwave or laser transmission and its use for in-space green propulsion.
Addressing the increasing need for in-space mobility, the work is structured across four dedicated Working Groups: WG1-Solar Cells, WG2-Wireless Power Transmission, WG3- In-Space Green Propulsion, and WG4- System Engineering, each tasked with defining strategic plans and clear objectives for the coming years, ultimately strengthening the EU’s leadership and strategic autonomy in space innovation.
Nanoelectronics for energy-efficient smart edge devices
UNIVERSITA DI PISA
IT, 36
TWO-Dimensional materials for ADvanced DevIces and low-power CompuTing
UNIVERSITAT POLITECNICA DE CATALUNYA
ES, 48
Unlocking the Full Potential of Edge AI through In-memory Computing Based on 2D Materials with Extreme Energy Efficiency
FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV
DE, 48
Spiking hybrid edge computing for robust optoelectronical signal processing
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
FR, 48
Ferroelectric 2D materials heterostructures for optical neuromorphic device functionalities
FUNDACION FUNDECYT – PARQUE CIENTIFICO Y TECNOLOGICO DE EXTREMADURA
ES, 48
Solid-state PCMs for Advanced energy Recovery and Key thermal control in electronics
THALES
FR, 48
Chiplet cOOling solutions for Low-consumption emBedded pRocessing AI at Nanoscale