Automated Scientific Discovery – Food (RAISE pilot)
Horizon Europe · Grant topic
Summary
A Polish company can use this call for R&D.
Funds multinational consortia developing trustworthy AI-driven laboratory automation and closed-loop experimentation demonstrators for food research.
- Who can apply
- Companies can lead: SME, large company, startup, research org, university.
- Amount
- €3,000,000Indicative budget
- Deadline
- 02 Feb 2027 01:00 CETin 101 days
- Status
- Openopened 13 Oct 2026
Machine-classified from the call text; check eligibility at the source.
Description
Expected Outcome:
Project results are expected to contribute to the following expected outcomes:
• 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;
• 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. The funded project 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 software 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.
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.
Impact areas of automated experimentation in biomass and precision fermentation could include the development of alternative protein sources in food production and alternative fats, bio-based materials, specialty carbohydrates, biotechnologies in food systems (such as biochemicals, microbial cultures, etc), food ingredients.
International collaboration is encouraged.
Who can apply, in detail
- Applicant types
- SME, Large company, Startup, Research org, University
- Purpose
- Research and innovation
- R&D relevance
- 3 of 3
- Poland eligible
- Yes
- Country limit
- —
- Region
- Any
- Consortium
- Required
- Readiness level (TRL)
- —
- Topics
- Food research
- Laboratory automation
- AI-driven experimentation
- Closed-loop scientific discovery
- Research data infrastructure
Machine-classified from the call text; check eligibility at the source. 23 Oct 2026
Conditions
Typed facts extracted from the source documents by Scindu's grant-data review. Each value keeps its own conditions. Unknown means unstated, not zero or unlimited.
- Applicant type
- 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.
- Eligible project scope
- Projects must develop safe and trustworthy closed-loop scientific experimentation systems integrating laboratory automation with AI-driven decision-making and robust data infrastructure, demonstrate retrofitting an existing advanced laboratory, include human oversight, and address the food/biotechnology impact area stated in the topic. Software development is prioritised; complementary equipment may be purchased only when justified.
- Other condition
- 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.
- Other condition
- The Work Programme expects around EUR 3.00 million per project and anticipates 1 project(s), but expressly permits proposals requesting different amounts. This is an indicative expectation, not a fixed project minimum or maximum.
- Other condition
- The catalogue row identifies this exact topic as HORIZON-RAISE-2027-01-02; the retained current normalized record marks it open. Official topic conditions are in the 2026-2027 Work Programme.
Amounts and costs
- Indicative budget
- €3,000,000
Topic budget as stated in the portal's budget table. Lump-sum and per-project limits are in the call conditions.
- Call allocation
- €30,500,000
- Eligible cost
- 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.
- Eligible cost
- 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.
- Eligible cost
- 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.
- Topic allocation
- €3,000,000
Personnel and services
Other role
Staff cost the grant can fund
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.
Source documents
Call facts
- Opens
- 13 Oct 2026
- Deadline model
- Single-stage
- Programme
- Horizon Europe · 2021 - 2027
- Call
- HORIZON-RAISE-2027-01 · AI in Science
- Types of action
- HORIZON Research and Innovation Actions
- Keywords
- AI
- AI Science
- AI in Science
- Alternative protein sources
- Biotech
- Food
- Food Systems
- Food ingredients
- Interdiscipinary Research
- Laboratory automation
- Precision fermentation
- RAISE
- Research automation
- Robotics
- Self-driving labs
- Source
- ec.europa.eu
- Checked
- 23 Oct 2026
- Portal updated
- 13 Oct 2026
- Review status
- Partial review
- Reviewed
- 23 Oct 2026
This is a summary of the source page, not the official text. Eligibility, conditions and the submission system are at the source.