SUSTAINABLE INNOVATION TECHNOLOGY SERVICES LIMITED

FARMWISE transforms the EU agricultural sector by empowering farmers and decision-makers with a state-of-the-art decision support system, combining precision agriculture, Artificial Intelligence, and remote sensing. FARMWISE provides new insights into water quality, quantity, soil health, and nutrient management. FARMWISE's state-of-the-art framework fosters knowledge sharing between scientists, farmers, and policymakers in a co-creation environment (Systems Thinking). It consolidates existing collaborations, and promotes new long-term cooperations between European organisations, including Water4All (BRGM) and Water Europe (SITES). The FARMWISE project will develop improved tools based on artificial intelligence (AI) for more efficient European water policy and decision-making founded on research-based technologies to solve the most burning water pollution and climate change problems. For this purpose, the FARMWISE consortium brings together the best European water, agricultural, climate, and AI researchers to handle the sustainability of water resources, the natural environment, and efficient agriculture in the highly diverse European landscape in view of present and future climate change. FARMWISE's impact will increase interest and bond cooperation of quadruple helix stakeholders for science-based solutions. FARMWISE evaluates, monitors, and implements gender and diversity balance in the consortium during the planned activities (T7.4). All partners' have established policies to foster gender equality, diversity, and inclusion for all employees. UEA holds a Silver Athena SWAN award in recognition of good practice in recruiting, retaining, and promoting women in STEMM subjects.

EC contribution:

EUR 246k

Start:

2024-01-01

End:

2026-12-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL6-2023-ZEROPOLLUTION-01

View on CORDIS (DOI 10.3030/101135533)

Europe’s transition to climate neutrality and a circular economy requires reliable and comparable tools to measure, reduce, and communicate product impacts. The EU’s Product Environmental Footprint (PEF) provides a common method, but uptake is limited by sectoral gaps, fragmented datasets, technical complexity, and insufficient links to eco-design and compliance. ORBIT responds by creating a six-layered innovation ecosystem to operationalise PEF across textiles, plastics/packaging, chemicals, and ICT/WEEE. It develops adaptive PEF Category Rules using machine learning for faster, iterative updates, and conducts full cradle-to-grave LCAs with socio-economic and end-of-life modelling. A FAIR-compliant data backbone and cloud-based platform automate LCA data flows, integrate ERP/PLM systems, and reduce barriers for SMEs. Explainable AI tools translate footprint results into actionable eco-design choices, while a Digital Product Passport & Compliance Engine ensures automatic alignment with ESPR, REACH, CIRPASS, and Green Claims. Impact will be demonstrated through sectoral pilots showing ≥30% footprint reductions in redesign scenarios. To foster uptake, ORBIT provides multilingual training via the ORBIT Academy, a stakeholder engagement hub, and SME-friendly validation pipelines. By combining leading research, AI specialists, industrial frontrunners, and policy actors, ORBIT will deliver validated, standard-aligned PEF tools and datasets that empower SMEs, accelerate compliance, and strengthen Europe’s global leadership in sustainable product policy and circular economy innovation.

EC contribution:

EUR 208k

Start:

2026-09-01

End:

2030-02-28

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL6-2025-01

View on CORDIS (DOI 10.3030/101291795)

Conventional polymer membranes are predominantly fossil-based, non-biodegradable, and fabricated with toxic solvents that pose risks to both human health and the environment. This creates a paradox where technologies intended for water treatment contribute to pollution. To overcome these challenges, there is an urgent need for environmentally sustainable, innovative membranes designed according to green chemistry and engineering principles. Green-Mem aims to pioneer fully bio-based and biodegradable membranes using renewable resources such as cellulose, chitosan, and natural fibres, replacing fossil-based polymers and hazardous solvents. The project will combine material science with machine learning (ML) to predict and optimise membrane properties, enabling scalable, energy-efficient fabrication while ensuring mechanical stability, biocompatibility, and high performance. Targeted applications include water treatment, antimicrobial filtration, and other areas requiring safe and selective separations. The consortium brings together 11 partners (6 academic, 5 non-academic) from Belgium, Türkiye, Ireland, Italy, Latvia, and Malaysia in an international, inter-sectoral, and interdisciplinary collaboration. Academic and industrial partners will jointly develop novel material combinations, optimise fabrication methods, and validate operational performance through pilot demonstrations. LCA and TEA will guide sustainable design, ensuring circularity and end-of-life strategies. Beyond scientific and technical innovation, Green-Mem is committed to open science, gender equality, and capacity building. Structured staff exchanges will foster knowledge transfer, enhance researcher skills, and create lasting research collaborations. By advancing eco-designed, recyclable membranes and aligning with the European Green Deal, the project supports sustainable growth, environmental leadership, and Europe’s global position in the circular materials economy.

EC contribution:

EUR 100k

Start:

2026-07-01

End:

2030-06-30

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-SE

Call:

HORIZON-MSCA-2025-SE-01

View on CORDIS (DOI 10.3030/101299479)

Rapid detection of survivors after an earthquake is critical to reducing fatalities, yet today’s search teams still rely on manual sensor placement, static probing, and intuition-based coverage. While sensor technologies such as microwave, seismic or acoustic, and visual systems exist, there is no established method for how to optimally place, move, and fuse them during real search operations. The AASE project will develop a new approach to earthquake victim search, using information-based active sensing strategies to guide first manual and later autonomous searches, taking into account region-specific conditions as well as universal principles applicable to earthquake response worldwide. Active sensing with cameras and seismic sensors specific to search missions will collect high-value data, which will then be fused in real time to inform rescuers about the likely positions of survivors, even in uncertain environments. From the outset, AASE will incorporate field knowledge from both governmental and internationally recognized non-governmental organizations, through qualitative studies and joint testing. Theoretical models and simulation-based validation will take place at the Middle East Technical University (METU) Center for Robotics and Artificial Intelligence (ROMER), followed by the Ministry of Interior’s physical disaster training grounds. In the final phase, ROMER’s legged robots will assist search operations, augmenting human capacity and reducing safety risks in the rubble. The fellow brings expertise in sensing and dynamic systems from the University of Washington and the University of Nevada, Reno, and through AASE will gain project management experience and emerge as an interdisciplinary academic in sensor-driven applications across Europe.

EC contribution:

EUR 0

Start:

2027-09-01

End:

2029-08-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-PF-EF

Call:

HORIZON-WIDERA-2025-TALENTS-01

View on CORDIS (DOI 10.3030/101334389)