Request for Proposals: Development of Artificial Intelligence & Machine Learning Training Courses for the European Materials Sector

EIT RawMaterials invites proposals from qualified organisations and consortia to develop self-paced digital training courses for the European Raw Materials Academy and European Advanced Materials Academy.

The assignment addresses 21 distinct artificial intelligence and machine learning skills gaps, all rated high priority, spanning industries from energy storage and renewable power generation to aerospace, construction and process manufacturing. Up to two providers may be appointed to develop practical, case-based training for professionals working across European materials value chains.

1. Purpose

The procurement will support the development of training that:

• Translates artificial intelligence and machine learning methods into applied practice for materials and manufacturing environments, rather than general AI literacy.
• Strengthens professionals' ability to work with real industrial data and to connect that data to the physical processes behind it, including predictive maintenance, process control and optimisation, automated inspection, property prediction and materials informatics.
• Uses real European industry cases, applied reasoning, and validation by named materials practitioners.
• Produces accessible, reusable, and updateable digital learning assets.

Courses must focus on practical implementation inside European plants, laboratories and supply chains rather than generic machine learning instruction. The primary target audience is working professionals (such as workshop employees, technicians, and leadership), with graduates entering the sector and professionals in adjacent engineering roles defined as secondary audiences, and policy makers as tertiary audiences.

2. Scope of Services

2.1. Raw Materials Academy Scope

Focuses on machine learning applied in live industrial operations:

• Predictive Maintenance & Condition Monitoring: Machine learning for the condition monitoring of industrial equipment and infrastructure.
• Process Control & Optimisation: AI-driven control and optimisation in refining and processing operations.
• Quality Control & Inspection: Automated, AI-powered inspection on production lines.

2.2. Advanced Materials Academy Scope

Focuses on research and production environments:

• Property Prediction & Design: Machine learning for materials property prediction, screening and inverse design.
• Materials Informatics: Accelerated discovery workflows, high-throughput synthesis and characterisation.
• AI-Enhanced Testing: Defect recognition in non-destructive testing.

Detailed skill codes and requirements are provided in the full RFP. Proposals must clearly identify the skills and subject areas addressed.

2.3. Applied Course Design and Delivery

Every course must provide practical, case-based learning grounded in adult learning principles. Course designs should include clear learning objectives, realistic industry scenarios, practical activities, structured feedback, and appropriate summative assessments. Expected learner effort in hours must be stated and justified.

Courses must be:

• Produced in British English
• Delivered as SCORM 1.2-compatible packages
• Tested for deployment on the Academy learning platform
• Designed in accordance with WCAG 2.2 accessibility standards and inclusive design principles
• Delivered with complete, transferable source files
• Inclusive of at least one Starter Pack (as described on the RfP)

Preferred production tools include the Articulate Suite (Storyline and Rise) and, where appropriate, Synthesia. Technical content must be supported by authoritative sources and validated by named Subject Matter Experts (SMEs) through a formal quality assurance process.

Applicants may propose supporting material as optional components. These must be separately scoped and priced, and may be awarded independently of a full course contract.

3. Contract and Eligibility

Proposals are capped at €100,000 excluding VAT per contract. Up to two contracts may be awarded to the same proposing entity for up to a maximum of €200,000. Detailed information about the pricing is accessible in the RfP.

Development, testing and acceptance must be completed within six months of contract signature.

Applications are open to organisations in EU Member States, EEA countries and Horizon Europe-associated countries. Consortia may apply if one organisation acts as the lead contracting party.

4. Required Capabilities and Experience

Applicants must demonstrate relevant technical expertise in circular economy, raw materials, or advanced materials manufacturing; experience in developing professional digital learning; and the capacity to deliver accessible, case-based, SCORM-compatible courses. Strong quality-assurance capabilities, expert validation procedures, and robust project management are also required.

5. Proposal Submission and Evaluation

Proposals must be submitted in English as a single PDF and include:

• A completed EIT RM Proposal Briefing Sheet
• The proposed course concept, target audience, learning objectives and learner hours
• The skill codes (SGA / SG IDs) and subject areas addressed
• The development approach, work plan, production tools, and delivery timeline
• The assessment, evaluation (Kirkpatrick framework) and certification approach
• Team details, named Subject Matter Experts (CVs), internal QA workflow, and examples of past work
• A complete financial proposal, with optional courses priced separately
• Information on GDPR compliance and liability insurance coverage
• A signed Tenderers' Declaration Form

Proposals must be submitted by email according to the instructions and deadline in the full RFP.

Proposals will be assessed by an evaluation panel against the award criteria and weightings stated in the full RfP. Applicants are not required to be partners of the EIT RawMaterials network, and all eligible proposals will be evaluated solely on their technical and financial merits.