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

1. Overview 

1.1. EU Raw Materials Academy and EU Advanced Materials Academy 

The European Raw Materials Academy (RM Academy) and the European Advanced Materials Academy (AM Academy) are flagship initiatives funded by the European Commission to support the upskilling and reskilling of Europe’s raw and advanced materials workforces. In response to growing demands for sustainability, digitalisation, and industrial resilience, the Academy aims to close critical skills gaps across the entire materials value chain — from exploration and processing to recycling and circularity, and substitution using advanced materials. Designed to serve professionals, SMEs, large enterprises, and public authorities alike, the Academy projects will offer a portfolio of high-quality, future-oriented learning programmes aligned with evolving labour market needs and EU policy priorities, such as the Critical Raw Materials Act, the Pact for Skills, the Net Zero Industry Act, and the Green Deal Industrial Plan. 

1.2. EIT Raw Materials 

EIT RawMaterials is a ‘Knowledge and Innovation Community’ (KIC) created by the European Institute of Innovation and Technology (EIT), aimed at promoting innovation in the raw materials sector across Europe. Established in 2015, EIT RawMaterials works to secure the sustainable supply of raw materials to the European industry by driving innovation, education, and entrepreneurship along the entire raw materials value chain.  

We are a knowledge-driven business and a catalyst for industrial progress. Our offerings leverage our expertise and that of our network – the world's largest network in the raw and advanced materials sector – which includes companies at every stage of evolution, from start-ups to market leaders, along with leading international universities, research organisations, and top experts and future talent from the sector. 

Our activities span from mining and mineral processing to material recycling and substitution, focusing on increasing resource efficiency and fostering a circular economy. We inform policy, apply knowledge, accelerate innovation, create opportunity, and unlock commercial value – for our partners and customers throughout the raw materials value chain to develop the raw materials sector as a strategic strength and foundation for a secure, sustainable future for Europe. Our offerings are designed to help our partners and industry to be part of Europe’s strategic agenda to ensure supply chain security and make the ‘Green New Deal’ a reality that benefits the people of Europe and partner nations. 

For more information about our company please visit the following website: 
https://eitrawmaterials.eu/

2. 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.

3. Scope of Services

3.1. 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.

3.2. 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.

3.3. 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.

4. Proposal Process

4.1. Participation 

Participation in this proposal procedure is open to all tenderers. Please complete the accompanying 
proposal template, entitled, “EIT RM Proposal Briefing Sheet”. 

All participants must sign the Tenderers’ declaration form attached and submit it with the proposal. 
Please note that the tenderer may not modify the text, it must be submitted signed as provided by EIT RawMaterials attached to this request for proposal document. 

4.2. Submission of proposal 

• Publication of the RfP: Aug 17, 2026 
• Deadline for questions: Sep 7, 2026 
• Answers published: Sep 14, 2026 
• Submission deadline: Oct 12, 2026 
• Evaluation and clarifications: Oct 26, 2026 
• Award decision and notification: Nov 9, 2026 
• Contract signature and start: Nov 30, 2026 
• Delivery of all courses: Contract-specific, but no later than six months of contract signature

Proposals should be submitted as a single PDF document by email to adnan.hardwarewala@eitrawmaterials.eu, with the subject line 'RfP AI and ML - [organisation name]'.