RAISE Doctoral Networks for AI in Science (RAISE pilot)

From the Commission's own budget table for this call. Confirm against the topic page before you plan around it: these move when a work programme is amended.

Expected Outcome: This action builds on the MSCA Doctoral Networks 2026 call (HORIZON-MSCA-DN-2026). The project results are expected to contribute to the following outcomes: For supported doctoral candidates: New research and transferable skills and competences in the application of AI in science, leading to improved employability and career prospects within and outside academia; New knowledge allowing the conversion of ideas into products and services, where relevant; Enhanced networking and communication capacities with scientific peers, as well as with the general public that will increase and broaden the research and innovation impact. For participating organisations: Improved quality, relevance and sustainability of doctoral training programmes and supervision arrangements; Enhanced cooperation and transfer of knowledge between sectors and disciplines; Increased integration of training and research activities between participating organisations; Boosted R&I capacity and uptake of AI in science; Increased internationalisation and attractiveness; Regular feedback of research results into teaching and education at participating organisations. Scope: Successful RAISE Doctoral Networks and doctoral candidates will be associated with RAISE, the Resource for AI Science in Europe. They will become part of the RAISE research community and will interact with its other members. As such, the scope of this call is limited to AI (artificial intelligence) in science, as outlined below. Proposals should therefore clearly specify how they meet this “AI in science” criterion. Doctoral candidates are to either develop or significantly participate in the development of innovative AI systems, models, tools or methodologies for their scientific domain. These systems, models, tools or methodologies must substantially innovate the way in which scientific information is analysed and enable a scientific contribution that furthers the state of the art in the discipline of the doctoral research. The development of the AI tool, model or methodology is to be an integral and indispensable part of the research work and be clearly delineated in the research work package. The scientific advancements of the project are to be directly dependent on the capabilities and application of these AI algorithms to drive the scientific inquiry, prediction, or understanding. Proposals are to achieve research results in the domain-scientific discipline of the doctoral research through using innovative AI techniques, going beyond a unique focus on computer-scientific AI development (e.g. to resul…