Automated Scientific Discovery - Materials Science (RAISE pilot) (RIA)
Horyzont Europa · Temat konkursu
Podsumowanie
Polska firma może skorzystać z tego naboru na B+R.
Funds multinational consortia developing AI-driven closed-loop experimentation systems and demonstrators in advanced materials science laboratories.
- Kto może złożyć wniosek
- Firma może być liderem: SME, large company, startup, research org, university.
- Kwota
- 26 500 000 €Budżet orientacyjny
- Termin
- 02.02.2027 01:00 CETza 101 dni
- Status
- Otwartyotwarty 13 paź 2026
Ocena maszynowa na podstawie tekstu naboru; warunki sprawdź u źródła.
Opis
Expected Outcome:
• 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.
Scope:
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.
The focus of this topic is on any aspect of materials science. Impact areas of automated experimentation in this field could include, non-exhaustively, battery technologies, photovoltaics, carbon capture/storage, water purification, soil remediation, drug discovery or sustainable fabrics.
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.
Kto może złożyć wniosek – szczegóły
- Typy wnioskodawców
- SME, Large company, Startup, Research org, University
- Cel
- Badania i innowacje
- Znaczenie dla B+R
- 3 z 3
- Polska kwalifikuje się
- Tak
- Ograniczenie krajów
- —
- Region
- Dowolny
- Konsorcjum
- Wymagane
- Poziom gotowości (TRL)
- —
- Tematy
- Materials science
- Autonomous laboratories
- AI-driven experimentation
- Closed-loop scientific discovery
- Research data infrastructure
Ocena maszynowa na podstawie tekstu naboru; warunki sprawdź u źródła. 23 paź 2026
Warunki
Fakty wyodrębnione z dokumentów źródłowych w przeglądzie danych Scindu. Każda wartość ma własne warunki. Brak danych oznacza, że źródło nie podaje wartości, a nie zero ani brak limitu.
- Typ wnioskodawcy
- Unless topic-specific conditions provide otherwise, RIA applicants must form a consortium of at least three independent legal entities established in three different countries: at least one in an EU Member State and two others in different Member States or Associated Countries. Affiliated entities do not count toward the minimum.
- Zakres projektu
- Projects must develop safe and trustworthy closed-loop scientific experimentation systems for advanced, automated and digitalised laboratories, integrating AI-driven decision-making and data infrastructures; the call requires proposals to demonstrate retrofit, experiment planning/execution/analysis and human oversight, and requires close interdisciplinary collaboration between computer/AI scientists and domain scientists. Equipment purchase is permissible only when justified and complementary; software development is prioritised.
- Inny warunek
- Single-stage call opened 13 October 2026 and has a deadline of 2 February 2027 at 17:00 Brussels local time. The Work Programme permits the Director-General to move opening by up to one month or deadline by up to two months; no later amendment was verified.
- Inny warunek
- The Work Programme expects around EUR 9.00 million per project and anticipates 3 project(s), but expressly permits proposals requesting different amounts. This is an indicative expectation, not a fixed project minimum or maximum.
- Inny warunek
- The catalogue row identifies this exact topic as HORIZON-RAISE-2027-01-01; the retained current normalized record marks it open. Official topic conditions are in the 2026-2027 Work Programme.
Kwoty i koszty
- Budżet orientacyjny
- 26 500 000 €
Budżet tematu z tabeli budżetowej portalu. Limity na projekt i ryczałty są w warunkach konkursu.
- Alokacja naboru
- 30 500 000 €
- Koszt kwalifikowalny
- Topic action type is Research and Innovation Actions. The current SEDIA action metadata identifies the corresponding Horizon grant model; the official Work Programme controls the topic-specific amount and conditions.
- Koszt kwalifikowalny
- Direct-contract natural persons and paid seconded persons may count as personnel only if assigned to the action, subject to general eligibility, working under conditions similar to employees and with results belonging to the beneficiary unless otherwise agreed. Their rate must reflect actual contract/secondment costs and not be significantly different from comparable employee costs.
- Koszt kwalifikowalny
- For employee personnel in budget-based actions, the MGA uses actual personnel costs: daily rate is the person’s annual personnel costs divided by 215, multiplied by identifiable action day-equivalents; annual reported EU-grant day-equivalents cannot exceed 215 less parental leave. This is an accounting formula, not a salary ceiling or offered wage.
- Alokacja tematu
- 26 500 000 €
Personel i usługi
Inna rola
Koszt personelu, który grant może sfinansować
The budget-based grant may include employees/equivalent, direct-contract natural persons, and seconded persons only subject to the MGA personnel conditions; SME-owner/natural-person beneficiary work uses the applicable unit-cost method. No topic-specific headcount, FTE, salary amount, pay cap, or employment duration is prescribed. This describes conditional cost eligibility, not a topic-mandated hire.
Dokumenty źródłowe
Dane naboru
- Otwarcie
- 13 paź 2026
- Tryb terminów
- Single-stage
- Program
- Horyzont Europa · 2021 - 2027
- Konkurs
- HORIZON-RAISE-2027-01 · AI in Science
- Rodzaje działań
- HORIZON Research and Innovation Actions
- Słowa kluczowe
- AI, RAISE, AI in Science, Advanced Materials, Robotics, Automated Labs
- Źródło
- ec.europa.eu
- Sprawdzono
- 23 paź 2026
- Aktualizacja portalu
- 13 paź 2026
- Status przeglądu
- Przegląd częściowy
- Przejrzano
- 23 paź 2026
To skrót ze strony źródłowej, nie tekst urzędowy. Kwalifikowalność, warunki i system składania wniosków są u źródła.