The 3rd Open call will select third party Collaborative Projects aimed at using the dAIEDGE Virtual Lab to solve specific industrial challenges defined by the project.
This call will be open to legal entities (Research and Technology Organisations, Academia or SMEs, including Startups) applying individually or as a consortium of up to 2 entities.
dAIEDGE 3OC Challenges
Challenges from the dAIEDGE consortium that drive research and development activities aimed at solving key industrial problems using advanced AI and edge AI technologies.
DOTSOFT OLOKLIROMENES LISEIS TECHNOLOGIAS PLIROFORIKIS ANONIMI ETAIREIA
Challenge: 9
Acronym: DARIUS
A federated edge-AI framework that leverages multi-city data to run “what-if” sensor-expansion scenarios, predicting impacts on urban comfort, traffic efficiency, and emissions.
International Hellenic University
Challenge: 4
Acronym: SPOT-LIFT
An embedded Bayesian AI agent for real-time robotic grasping and manipulation, optimised for on-device GPU execution and robust operation under uncertainty in dynamic urban environments.
Intigia S.L.
Challenge: 11
Acronym: PYNQ-OPT
SWaP optimized AI models for complex tasks: optimize AI models for vehicle detection on Zynq 7020 and Zynq Ultrascale
Limited Liability Company "Bezryma Group"
Challenge: 8
Acronym: OMPF-EDGE
Edge AI for real-time, privacy-first workout & rehabilitation movement monitoring to prevent injuries and improve health through personalized insights.
Next Prototypes e.V.
Challenge: 4
Acronym: BRAINS
Enabling energy-efficient humanoid locomotion through Bayesian inference on embedded edge systems.
Sensifai BV
Challenge: 3
Acronym: Q-WiSE
Energy-efficient, quantized deep Wiener-filter AI for real-time speech enhancement on ultra-low-power edge devices.
Sunesis d.o.o.
Challenge: 1
Acronym: EDGEWISE
AI-native, context-aware and dynamic orchestration system for deploying and balancing trustworthy and energy-efficient federated learning across the entire IoT–Edge–Fog–Cloud continuum
SYPHOS LP
Challenge: 12
Acronym: SPARTA
Image classification with fully sparsified Vision Transformers by combining unstructured pruning, sparsity-enhancing architectural adaptations and specialized sparse-aware FPGA configurations
