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Welcome to the ESA Φ-lab Collaborative Innovation Network

The Collaborative Innovation Network (CIN) by ESA Φ-lab, managed in partnership with Pi School, is a dynamic community that brings together leading researchers and innovative talents to harness transformative technologies and accelerate the future of Earth Observation.

The ESA Φ-lab CIN aims to establish a global network where researchers and innovators can collaborate with each other and with ESA, sharing knowledge and developing groundbreaking EO solutions. By fostering cross-disciplinary partnerships, CIN enhances the impact of ESA Φ-lab's innovation efforts and drives advancements in EO technology and its applications.

Interested in getting involved? Explore our opportunities or send an unsolicited proposal to esa-philab-cin@picampus-school.com.

News & Events

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Upcoming and Latest
Φnnovation Summit: Disruptive Innovations for Earth ObservationΦnnovation Summit: Disruptive Innovations for Earth Observation
Φnnovation Summit: Disruptive Innovations for Earth Observation
Why do cloud structures change? Insights from EarthCARE on closed-to-open cell transitionsWhy do cloud structures change? Insights from EarthCARE on closed-to-open cell transitions
Why do cloud structures change? Insights from EarthCARE on closed-to-open cell transitions
The Future of Earth Intelligence: Edge Computing, GeoAI, and the Architecture of Earth Observation's Next DecadeThe Future of Earth Intelligence: Edge Computing, GeoAI, and the Architecture of Earth Observation's Next Decade
The Future of Earth Intelligence: Edge Computing, GeoAI, and the Architecture of Earth Observation's Next Decade
Towards a more sustainable training of Foundation Models for Earth observationTowards a more sustainable training of Foundation Models for Earth observation
Towards a more sustainable training of Foundation Models for Earth observation
Self-supervised learning for SAR images: from despeckling to representation learningSelf-supervised learning for SAR images: from despeckling to representation learning
Self-supervised learning for SAR images: from despeckling to representation learning
Learning to generalise across modalities and scales for forest remote sensingLearning to generalise across modalities and scales for forest remote sensing
Learning to generalise across modalities and scales for forest remote sensing
Training and deploying pre-trained models for remote sensing dataTraining and deploying pre-trained models for remote sensing data
Training and deploying pre-trained models for remote sensing data
GeoSANE: Learning Geospatial Representations from Models, Not DataGeoSANE: Learning Geospatial Representations from Models, Not Data
GeoSANE: Learning Geospatial Representations from Models, Not Data
Towards Sensor-Informed Foundation Models for Synthetic Aperture Radar DataTowards Sensor-Informed Foundation Models for Synthetic Aperture Radar Data
Towards Sensor-Informed Foundation Models for Synthetic Aperture Radar Data
EVE: an Open Source Earth Science LLM for Researchers, Policymakers, and the PublicEVE: an Open Source Earth Science LLM for Researchers, Policymakers, and the Public
EVE: an Open Source Earth Science LLM for Researchers, Policymakers, and the Public
From AI-eXpress to Cognitive Cloud Computing in Space (3CS): Building Intelligent, Interconnected EO ConstellationsFrom AI-eXpress to Cognitive Cloud Computing in Space (3CS): Building Intelligent, Interconnected EO Constellations
From AI-eXpress to Cognitive Cloud Computing in Space (3CS): Building Intelligent, Interconnected EO Constellations
AI-based Downscaling of Terrestrial Water Storage Grids from Simulated NGGM and MAGIC DataAI-based Downscaling of Terrestrial Water Storage Grids from Simulated NGGM and MAGIC Data
AI-based Downscaling of Terrestrial Water Storage Grids from Simulated NGGM and MAGIC Data
Bringing SAM to new heights: leveraging elevation data for tree crown segmentation from droneBringing SAM to new heights: leveraging elevation data for tree crown segmentation from drone
Bringing SAM to new heights: leveraging elevation data for tree crown segmentation from drone
Machine Learning at the Large Hadron ColliderMachine Learning at the Large Hadron Collider
Machine Learning at the Large Hadron Collider
Multi-Objective AutoML: Towards Accurate and Robust EO modelsMulti-Objective AutoML: Towards Accurate and Robust EO models
Multi-Objective AutoML: Towards Accurate and Robust EO models
Open Source Software for Quantum TechnologiesOpen Source Software for Quantum Technologies
Open Source Software for Quantum Technologies
Accelerating Earth Observation with High-Performance ComputingAccelerating Earth Observation with High-Performance Computing
Accelerating Earth Observation with High-Performance Computing

See all News & Events!

Open Opportunities: Visiting Researchers, Visiting Professors, and Experts at ESA Φ-lab

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Upcoming and Latest
Visiting Researchers in onboard AI for Φsat-2 missionVisiting Researchers in onboard AI for Φsat-2 mission
Visiting Researchers in onboard AI for Φsat-2 mission
November 5, 2025
Call for Visiting Researchers in Agentic AI SystemsCall for Visiting Researchers in Agentic AI Systems
Call for Visiting Researchers in Agentic AI Systems
August 8, 2025
Call for Visiting Researchers in Quantum TechnologiesCall
Call for Visiting Researchers in Quantum Technologies
August 8, 2025

See all CIN Calls!

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