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Find similar grantsHorizon Europe RAISE Automated Scientific Discovery is sponsored by European Commission (Horizon Europe). This Horizon Europe topic (HORIZON-RAISE-2027-01-01) funds research in automated scientific discovery using AI.
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Automated Scientific Discovery (RAISE pilot) (RIA) | Horizon-europe. gouv.
fr Recherche en texte intégral Présentation du programme Les programmes de travail La coopération internationale Autres programmes de financement Les Points de Contact Nationaux Aides au montage de projets Ressources juridiques et financières Être expert auprès de la CE Conseil européen de la recherche (ERC) Actions Marie Sklodowska-Curie (MSCA) Infrastructures de recherche Cluster 2 - Culture et société Cluster 3 - Sécurité civile Cluster 4 - Numérique, industrie et espace Cluster 5 - Climat, énergie et mobilité Conseil européen de l'innovation (EIC) L'institut européen d'innovation et de technologie (EIT) Écosystèmes européens d'innovation (EIE) Statistiques de participation au PCRI scanR, l'outil de cartographie thématique R&I Dashboard, l'outil de la C.
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2026 au 05. 02. 2026 Evènement d'envergure nationale qui vise à fédérer les entreprises innovantes d’Auvergne-Rhône-Alpes et à mettre en lumière les acteurs clés de l’écosystème technologique et entrepreneurial.
Ce topic appartient à l'appel Automated Scientific Discovery (RAISE pilot) (RIA) HORIZON Research and Innovation Actions Call Identifier : HORIZON-RAISE-2027-01 Development of closed-loop scientific experimentation systems that integrate automation with AI-driven, trustworthy decision-making processes in existing laboratory environments; Accelerated scientific discovery with increased efficiency and reproducibility; Improved scientific productivity; Advancement of laboratory automation, including development of best practices, challenges, and opportunities for accelerating R&D; and Prototype functional demonstrators that showcase the integration of automation with AI-driven decision-making, enabling the development of closed-loop scientific experimentation systems.
This topic addresses the development of safe and trustworthy closed loop scientific experimentation systems through the integration of laboratory automation with AI-driven decision-making processes and robust data infrastructures.
Funded projects will help scientific labs with an already advanced level of automation and digitalisation to design, develop, and test the intelligence layer that enables scientific instrumentation to semi- or fully autonomously plan, run, and analyse experiments, ideally in coordination/network with other labs and without requiring a complete redesign of existing laboratory outfitting.
Proposals will incorporate comprehensive data management systems capable of handling the collection, storage, processing, and sharing of experimental data. This includes developing scalable and secure data storage solutions, efficient data processing and analysis tools, and mechanisms to facilitate data sharing and collaboration across labs, while ensuring data security and privacy.
Systems could incorporate AI-driven resource optimisation modules, actively minimising energy, reagent, and material consumption during automated experimentation cycles. Systems should incorporate appropriate level of security and robustness by design.
Proposals should demonstrate how an existing lab can be retrofitted with AI-driven systems to plan, execute, and analyse experiments in a closed-loop fashion, incorporating human oversight and interaction to ensure accuracy, safety, and ethical compliance.
Possible research targets include (non-exhaustively): Autonomous/semi-autonomous and adaptive AI systems (including agentic AI) that connect with laboratory instruments and robotics and can autonomously plan, act, learn and adapt within a scientific environment, within a validated safe pipeline, Assistive and interactive safe AI-managed robotic systems that automate diverse experiments and can be applied to a diverse hardware setup.
Scalable automation solutions and networked AI systems that enable collaborative experimentation across multiple labs and networks of labs (including different geographic locations), supporting the simultaneous execution of large volumes of experiments, Systems that provide real-time data processing and analytics, enabling immediate feedback and dynamic adjustments during experiments Standards and protocols to ensure interoperability between different laboratory instruments, robotics, and AI systems Intuitive user interfaces for enhanced human-machine interaction AI-driven predictive maintenance systems to optimize equipment uptime and resource utilization.
Exploration of how scientific automation technologies can be adapted for use in various scientific disciplines beyond those in the scope of this call Proposals should demonstrate close interdisciplinary collaboration of computer/AI scientists and domain scientists.
While the scope of this call prioritises software development, it does not exclude the justified purchase of complementary equipment necessary to implement the research targets of the project. An initial focus on materials science is put forward (Cluster 4).
Impact areas of automated experimentation in this field could include (non-exhaustively) drug discovery, battery technologies, photovoltaics, carbon capture/storage, water purification, soil remediation, environmentally friendly fertilizers, development of alternative protein sources in food production, sustainable fabrics/dyes.
The thematic focus of this topic can be expanded to include scientific disciplines and experimental settings of interest to collaborating clusters. International collaboration is encouraged. Proposals are expected to develop synergies with running Horizon Europe projects in the same field, for example with HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61.
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Based on current listing details, eligibility includes: Consortia from EU Member States and Horizon Europe Associated Countries. Requires collaborative multi-partner research networks. Open to universities, research institutions, and industry partners. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Funding amounts vary based on project scope and sponsor guidance. Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is February 1, 2027. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
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The GenAI4EU Booster is a Horizon Europe call under Cluster 4 (Digital, Industry and Space) focused on the real-world deployment of generative AI in specific industrial sectors with measurable outcomes. The total budget is approximately EUR 30 million. Projects must address specific industrial challenges with end-user organizations, demonstrating how generative AI — including large language models, multimodal models, and generative design tools — can transform production, services, and innovation in sectors such as manufacturing, energy, healthcare, and agriculture. The programme requires consortia to include end-user organizations that will deploy the resulting generative AI solutions, ensuring practical impact beyond academic research. This is part of the broader GenAI4EU initiative, Europe's strategy to build sovereign generative AI capabilities. The deadline for applications is April 15, 2026. This call sits under the AI, Data and Robotics Partnership within Horizon Europe's Cluster 4 programme.
This Horizon Europe call under Cluster 4 (Digital, Industry and Space) seeks to develop advanced robotics skills leveraging next-generation AI and generative models for manufacturing applications. The total budget is EUR 18 million. Projects should develop core robotic capabilities including high-speed autonomous pick-and-place, physical manipulation, and human-robot collaboration by leveraging next-generation AI architectures and generative models. The goal is to create modular and industry-agnostic frameworks that can be easily reconfigured for diverse manufacturing environments, with a mandatory dedicated use case in the automotive industry. Projects are expected to target TRL 4-6 (validation in lab to demonstration in relevant environment). This call sits under the AI, Data and Robotics Partnership, meaning proposals must align with the partnership's strategic research and innovation agenda. The deadline for applications is April 15, 2026.
Fire Science Innovations through Research and Education (FIRE) program is sponsored by National Science Foundation (NSF). This program invites innovative multidisciplinary and multisector investigations focused on convergent research and education activities in wildland fire. It supports research that can inform risk management and response, adaptation, and resilience across infrastructures, communities, cultures, and natural environments. Relevant topics include developing novel materials and methods for retrofitting existing buildings and remediating buildings following wildfire and smoke events.
The UKRI Policy Fellowships 2025, funded by the Economic and Social Research Council, offer 18-month placements for academics to co-design research with UK government and What Works Network host organizations. Awards range from £180,000 to £280,000 and support three fellowship tracks: core policy fellows, Natural Hazards and Resilience policy fellows, and What Works Innovation fellows. Applicants must hold a PhD or equivalent research experience, be based at a UKRI-eligible UK organization, and possess relevant subject matter or methodological expertise. Government-hosted positions target early to mid-career academics, while What Works fellowships welcome all career stages. Fellows work directly with policymakers to bridge academic research and policy development on pressing national and global challenges. The application deadline is July 15, 2025.