Alla kurser

Enhance your knowledge in sustainable metal recycling – a course for the engineers of the future. In a world where circular material flows are becoming increasingly important, metal recycling plays a crucial role. This course gives you an understanding of metallurgical processes – the core of transforming complex residues and scrap into new, valuable metals and alloys.

Why take this course?

Metal scrap and industrial by‑products vary greatly in composition and quality. To meet market demands, both technical knowledge and insight into how design, material selection, and processes interact are needed.

In this course, you will gain:

• Practical and theoretical knowledge of the metallurgical processes used in metal recycling
• Knowledge of copper recovery from electronic scrap and steel recycling from end‑of‑life vehicles
• Insight into how by‑products are generated – and how they can be utilized in new applications, for example in construction materials
• Perspectives on how product design affects recyclability throughout the product’s life cycle

Flexible learning – designed for working professionals

The course is structured to be compatible with full‑time work and is based on:

• Self‑study through web‑based materials, videos, and literature
• Scheduled webinars and digital seminars
• Project work and reflective assignments that translate theory into practical understanding

All teaching is conducted online – no physical attendance is required.

The course will enable you to:

1. Know the concept of sustainable development
2. Know some of the most common techniques used in recycling processes for metal recycling
3. Understand the connection between process choice and material composition
4. Be aware of how residual products are generated in high-temperature metallurgical processes and have knowledge of possible areas of use
5. Know the limitations for recycling different types of metal-containing scrap and the use of metal-containing residual products
6. Know how the design of a product affects the recycling of the end-of-life product

Who is this course for?

The course is designed for professional engineers in industry and research institutes, consultants, sustainability and environmental specialists. As well as other professionals with an interest in recycling, metallurgy, and the circular economy and want to deepen their knowledge of process metallurgy.

Practical information

• Entry requirements:
  • 180 ECTS in engineering or natural sciences
  • English 6/English B
  • or equivalent professional experience

Start October 15, 2026
Studyhours 80h
Studypace 25%
Location Online, ortsoberoende
Coursefee 0 sek
Luleå University of Technology and Örebro University

This course is given by Mälardalen university in cooperation with Luleå University of Technology.

This course is given by Mälardalen university in cooperation with Luleå University of Technology.

Kursen är under utveckling och planeras för januari 2027.

Kursen är under utveckling och planeras för start våren 2027.

This course introduces modern reinforcement learning methods for learning control policies in simulated environments. The course presents the reinforcement learning framework for sequential decision-making and covers widely used deep reinforcement learning algorithms, including valuebased methods such as DQN/DDQN and actor–critic approaches such as Proximal Policy Optimisation (PPO) and Soft Actor–Critic (SAC). The focus is on how neural network policies are trained through interaction with simulated systems.

The course includes a practical component in which reinforcement learning agents are trained in physics-based simulation environments. Students train baseline agents using standard reinforcement learning algorithms and evaluate the resulting performance in simulation.

The central project of the course focuses on improving the performance of the trained agents through iterative optimisation. Large language models (LLMs) are used as tools to propose modifications to the learning system, which are then implemented and evaluated through repeated training runs.

The course contributes to the goals of the green transition by focusing on optimisation methods for complex dynamic systems. Reinforcement learning enables automated optimisation of sequential decision processes and can be applied to improve efficiency in systems where reducing energy consumption and resource usage is critical.

Start autumn 2026
Studyhours 80h
Studypace 25 %
Location Online, ortsoberoende
Course fee0 sek
Örebro University and Luleå University of Technology

This course introduces the principles and practice of human-AI teaming with a particular focus on industrial settings. The course addresses how humans and AI systems can work together as collaborative partners rather than as simple tool-users. The most common case of a human-AI team consists of one human collaborating with one AI agent, yet also, multiple autonomous robots supervised by a human can be developed into a human-AI team. Course participants will study how different building blocks of human-AI teaming can be used, combined to develop as well as to evaluate those teams.

The course combines conceptual frameworks with applied case studies relevant for industry. Participants will get to know about Fexisting frameworks that may serve as starting points for human-AI teaming, such as human-centered AI, levels of autonomy, , joint cognitive systems and multi-agent systems. Participants will learn about ractical models for evaluating trust, transparency, responsibility, and teamwork performance. Case studies will be drawn from industrial domains such as manufacturing, process optimization, logistics, decision support, robotics, and monitoring and control environments. The intention is to help participants understand when AI should advise, when it should act proactively, and how accountability and control should be distributed between humans and AI. The course is organized in modules that move from foundational concepts to application. Early modules introduce human-AI teaming as a design and organizational challenge, including legal constraints e.g. related to safety in human-AI teams, This is followed by modules on building blocks such as trust calibration, communication and explainability, contextual and situation awareness, initiative and proactive support and role adaptation, as well as individual planning for teamwork. A third block of modules deals with team-level questions, introducing coordination and adaptation between team members over time, team modelling and evaluation of team performance, robustness. This is accompanied by modules that focus on industrial cases, Throughout the course, students analyze examples of successful and unsuccessful deployments and reflect on how human-AI teaming can be designed responsibly in practice. Examination is proposed through a combination of seminar participation, a written reflection or short analytical assignment linked to the literature and frameworks, and a final case-based project in which participants analyze or design a human-AI teaming scenario for an industrial context. This examination supports both conceptual understanding and the ability to apply theory to realistic problems.

The course is central to the scope of REEDEAM because the green and digital transitions require not only advanced AI technologies but also competent integration of those technologies into human work practices. Industrial transformation depends on people being able to collaborate effectively with increasingly capable AI systems in ways that are trustworthy, safe, context-aware, and value-creating. By equipping students and professionals with the ability to understand, evaluate, and design human-AI teaming solutions, the course contributes directly to competence development for future sustainable and digitally enabled industry.

Start autumn 2026
Studyhours 80h
Studypace 25 %
Location Online, ortsoberoende
Coursefee 0 sek
Örebro University

This course introduces AI-assisted coding (“vibe coding”) as a practical skill for industrial professionals seeking to leverage large language models (LLMs) in their daily work. Organized across five lessons of one hour each, the course takes participants from a conceptual understanding of how LLMs function to hands-on use of modern AI coding environments, prompt engineering, version control, debugging, and security awareness.

The course covers five thematic areas: (1) an introduction to LLMs, their probabilistic nature, the black-box problem, and the hallucination risks; (2) setting up AI coding environments such as Cursor, Windsurf, and Claude Code, alongside foundational prompt engineering techniques including context-setting, constraint specification, and iterative refinement; (3) advanced prompt engineering best practices covering code readability, version control with tools like GitHub, and key limitations of LLMs; (4) a full practical pipeline including understanding, modifying, testing, debugging, and documenting code, as well as advanced features such as multi-agent workflows, RAGs, and MCP; and (5) security considerations including LLM-generated vulnerable code, data privacy, and prompt injection risks. Each lesson is paired with live coding demonstrations, and Lesson 4 features a complex end-to-end example. Assessment is based on participation and a practical coding project.

The course directly addresses the REEDEAM mission by equipping industrial professionals with AI coding skills that can be applied to sustainability challenges. By lowering the barrier to software development through vibe coding, participants from manufacturing, energy, and process industries can build their own data tools, automate repetitive tasks, and implement solutions that monitor and reduce environmental impact. The course promotes critical, informed use of AI, as opposed to blind automation, ensuring participants understand both the power and the risks of LLM-generated code in industrial contexts.

Start September 7, 2026
Studyhours 80h
Studypace 25 %
Location Online, ortsoberoende
Course fee 0 sek
Örebro University

This course introduces machine learning and deep learning methods for professionals working in mining and geology. Each topic is introduced through a real challenge from the mining industry, spanning the full value chain from exploration and drilling through to resource estimation and production. The program covers a broad range of methods including classical machine learning, convolutional neural networks, generative models, and large language models, all taught in the context of geological workflows so participants always see how a method connects to something they already do in practice.

The course is structured across six modules, moving from the fundamentals of geological data and the ML pipeline, through classical methods such as Random Forest, XGBoost, into deep learning with CNNs for drill core image analysis and mineral segmentation, and finally generative approaches including GANs, diffusion models, and large language models for data enhancement and augmentation. Throughout, participants work with mostly work with geological data using practical tools like Python, PyTorch, and scikit-learn. The goal is not to turn geologists into data scientists, but to allow the participants to evaluate and integrate such tools into their daily work, make them informed and critical users.

The course tightly connects to the goals of the green transition. Mining is resource and energy intensive, and better data-driven decision-making can have a positive impact. By learning to apply ML tools to various problems, such as exploration targeting, grade estimation, and mineral identification, participants gain the ability to help reduce unnecessary drilling, improve resource efficiency, and lower the overall environmental footprint of mining operations.

Start November 4, 2026
Studyhours 80h
Studypace 25 %
Location Online, ortsoberoende
Coursefee 0 sek
Örebro University and Luleå University of Technology

This course provides an introduction to modern organic analytical techniques used to investigate organic contaminants in the environment. Participants will gain knowledge about key steps in the analytical workflow, including environmental sampling, organic sample preparation, method validation, quality assurance and quality assurance, and instrumental analysis. An essential component of the course is method validation and the implementation of quality control measures for producing reliable and reproducible results. Participants will explore concepts such as precision, accuracy, and detection limits, gaining the ability to assess analytical performance and ensure the reliability of results. Basic principles of chromatographic and mass spectrometric separation is covered. Participants will explore qualitative and quantitative methods for analytical data evaluation and understand how data is influenced by analytical choices, sample matrix effects, and instrumentation parameters.

The course closely aligns with REEDEAM’s objectives by providing participants with an important knowledge foundation. The course helps fill critical skills gaps in analytical chemistry and environmental monitoring, areas identified as bottlenecks for industry’s climate transition. By finishing this course, participants will have an increased understanding about organic analytical chemistry, valuable for industries undergoing green transitions in recycling, manufacturing, and sustainable consumption.

The course provides advanced analytical skills needed by industry to address challenges in resource efficiency, pollution control, process optimization, and regulatory compliance-key aspects of the green transition that REEDEAM targets.

The course will be examined with a written digital exam.

Start September 14, 2026
Studyhours 80h
Studypace 25 %
Location Online, ortsoberoende
Studyfee 0 sek
Örebro University

With this course, participants will be introduced to the basic principles and applications of inorganic contaminants and their relation to biogeochemical processes in the environment. The modular principle of this course will guide participants through the necessary theoretical knowledge about analysis, distribution and environmental behaviour of inorganic elements. Modules in theoretical analysis will enable participants to learn about the practical means involved when assessing inorganic contamination. They will gain familiarity with the relevant field work techniques and basic and advanced analytical methods for quantification. A model on chemical modelling and data evaluation completes the analytical fraction of the course. The course integrates modern analytical techniques, environmental chemistry, and case studies from biogeochemistry. The examination will be held as digital written exam.

Start November 9, 2026
Studyhours 80h
Studypace 25 %
Location Online, ortsoberoende
Course fee 0 sek
Örebro University