RAISE Doctoral Networks for AI in Science (RAISE pilot)

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 result in publications only in computer-scientific AI-related venues) or the use of existing AI systems, methodologies or general-purpose computational tools for data processing/statistical analysis in an instrumental way. All doctoral candidates should receive dedicated doctoral-level training on AI in science (understood as outlined above). In order to apply for the RAISE Doctoral Networks call, applicants must submit their proposal to the Marie Skłodowska-Curie actions (MSCA) Doctoral Networks 2026. At proposal stage, applicants will be able to declare their interest in being considered for the RAISE Doctoral Networks and, if so, they should demonstrate that their proposal falls within the scope of the RAISE call. The proposals submitted under the RAISE Doctoral Networks must fulfil all the admissibility and eligibility conditions of the MSCA Doctoral Networks 2026. The RAISE proposals will be evaluated applying the MSCA award criteria and the MSCA scoring system. At the end of the MSCA evaluation procedure, a cross-panel list of fundable RAISE proposals will be established with the highest-ranking reserve list proposals. The list would be ranked according to the MSCA evaluation scores and comprise proposals with a requested budget as close as possible to 3 times the available budget. Experts will assess the relevance of the proposals on this list to the RAISE topic. The final ra