Thematic Networks of Excellence for AI in Science - Agriculture and Environmental Pollution (RAISE pilot)

Expected Outcome

Project results are expected to contribute to the following expected outcomes:

• scientific progress with the help of AI, addressing strategic scientific challenges in the thematic areas selected (agricultural sciences and environmental pollution sciences);
• pilot network of excellent labs in the selected thematic areas to pool talent and expertise as part of a Resource for AI Science in Europe (RAISE), operating as a virtual institute across Europe;
• establish a model of cooperation among these labs and support the development of a strategic research agenda for the application of AI in scientific research in the selected thematic domains;
• reinforce the European AI in science community, attract top-notch talent and spread excellence in AI in science;
• develop synergies with the “Science for AI” RAISE pilot call.

Scope

The aim of this topic is to establish a network of excellent labs across Europe, dedicated to collaborative research using AI in strategic and promising scientific areas or domains, piloting RAISE and aligning research efforts.

The selected consortium will be composed of leading European research labs in the thematic area with strong experience in applying AI in the research process. At least half the members of the consortium should have a proven track record of developing innovative AI solutions for scientific research. Consortia are welcome to involve expertise in Social Sciences and Humanities in their proposal as they see fit to achieve the objectives.

The network of excellence will be dedicated to the application of AI to scientific research in a agricultural sciences and environmental pollution sciences (Cluster 6).

To achieve these objectives, the consortium will undertake a range of dedicated activities:

• drawing on the consortium's knowledge and excellence, propose an ambitious strategic research agenda for the field, beyond the research agenda of the consortium in the context of the project, identifying key scientific grand challenges that can be addressed with AI in the thematic area of the network;
• carry out collaborative research to solve the prioritised scientific challenges, through explicit targets and milestones;
• as part of RAISE as a virtual institute, closely work with central RAISE governance and the other RAISE networks of excellence as privileged partners for sharing data, research results, expertise and infrastructure;
• develop talent and knowledge exchange schemes (e.g. jointly supervised fellowships, mobility schemes, summer schools, matchmaking events) and include partnering schemes with institutes outside the excellence network to spread excellence across Europe;
• identify, expand, curate, integrate share and make available relevant datasets and AI models. Develop community-driven standards and benchmarks for AI models in the thematic scientific area or discipline;
• develop collaborations with industry for uptake of scientific outcomes and AI-based research methodologies.

The consortium should use financial support to third parties (FSTP), in order to spread excellence and seek complementary expertise across Europe. The FSTP should bring complementary/supplementary data, expertise, AI models, etc. The consortium should set up an open and objective selection process to complement the efforts of the consortium for a maximum of EUR 60,000 per third party. Beneficiaries are expected in addition to spread excellence by transferring knowledge to FSTP recipients.

The core consortium’s activities should represent a minimum of 85% of the proposed budget. FSTP recipients should be established in a Member State or an Associated Country. FSTP activities should be integrated into (one or more) separate work packages, which are to describe the conditions for implementing the support.

Proposals are expected to develop synergies with running Horizon Europe projects in the same field, notably with the projects on AI foundation models in agricultural sciences and environmental pollution sciences funded under topic HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61: AI Foundation models in science (GenAI4EU).