All courses

Why do we have markets for electricity? How do they function? This introductory course explains how incentives shape outcomes in the electricity market. It brings out the implications for businesses and society of electricity pricing in the shadow of the energy transition.

The course aims to provide a comprehensive overview of the electricity market’s role in ensuring an efficient electricity supply and addressing key public questions, such as

• What is the purpose of the electricity market?
• Why do electricity prices vary by location?
• How can electricity prices surge despite low production costs?
• Are there alternative ways to sell electricity?
• Why is international electricity trading important?

The course emphasizes the role of economic incentives in shaping market behavior and addresses critical issues such as market power and its consequences. You will also explore the inefficiencies stemming from unpriced aspects of energy supply and the role of regulation in mitigating these inefficiencies.

As the global push toward decarbonization accelerates, the course delves into the challenges posed by large-scale electrification, the implications of climate legislation for energy systems, and the impact of protectionist national policies.

The course offers a comprehensive introduction to the electricity market, provides you with analytical tools for independent analysis and brings you to the forefront of current energy policy debate.

You will learn

• Describe the interaction between the electricity system and the electricity market.
• Explain how the electricity market can increase the efficiency of electricity supply, e.g. with respect to market integration.
• Show how market power reduces the efficiency of the electricity market.
• Categorize fundamental market imperfections and describe their solutions.
• Explain economic and political challenges associated with the green transition.
• Apply economic tools to analyze the electricity market and examine how changes to the electricity system and regulation affect market outcomes.

Having good skills in development work is becoming increasingly important in professional life. This course gives you the opportunity to develop knowledge and ability in product, production, and business development, as well as the relationship between them.

Skills in circular business models are becoming increasingly important in professional life. This course gives you specialized knowledge and skills in designing and implementing circular business strategies.

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

Renewable hydrogen stands out as a highly promising solution to decarbonize heavy industries and transportation sector, helping to achieve the climate goals of Sweden- reaching net zero emissions by 2045.
The terms renewable hydrogen, clean hydrogen or green hydrogen refers to hydrogen produced from renewable energy or raw material. The utilization of renewable hydrogen for industrial applications necessitates the development of the entire value chain, from generation and storage to transport and final applications. Unlocking the potential of hydrogen economy in Sweden involves not only technological advancements and infrastructure development but also a skilled workforce.

This course offers an introduction of renewable hydrogen as a pivotal component for industrial applications, focusing on its generation, storage, transport, and utilization within industrial contexts. You will gain a comprehensive understanding of the technical, economic, and environmental aspects of renewable hydrogen technologies, such as electrolysis, fuel cell, and hydrogen storage and distribution solutions, preparing them with essential knowledge and foundational insights for advancing the decarbonization of industrial processes through the adoption of hydrogen-based energy solutions.

The course gives an introduction to how hydrogen can be produced sustainably, focusing on water electrolysis and biomass gasification.

Kursen Ledarskap i hållbar förändring 1 – Förändringstänkande och reflektiva kompetenser, är en kurs för dig som aktivt vill delta i den industriella och samhälleliga förändringen mot hållbarhet.

The course focuses on developing self-leadership, increasing your reflective ability, making you aware of the learning in your experiences, and mastering your further personal development as a transformative actor and leader.

The course is built around six separate tasks that are done individually. The tasks come from a task bank where you choose which ones to do and in what order. The course ends with a summary task where you gather what you’ve learned from the six tasks you completed.

The course is part of a package of four courses, all aimed at developing your practical and theoretical skills so you can take on a role as a transformative agent and leader at your workplace and in society. The courses are launched gradually and are continuously improved based on your feedback and how well they’re received.

The courses are designed to be flexible, and you can participate even if you work. You can complete the course assignments in your own order and at your own pace on a digital platform.

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

The courses are designed to be flexible, and you can join in even if you have a job. You can do the course assignments in any order and at your own pace on a digital platform.

The course aims to develop your action skills – to go from words to action, boost your initiative, and drive the development of your experience-based knowledge in your role as a transformative agent and leader.

The course is built around six separate tasks that are completed individually. The tasks come from a task bank where you choose which tasks to do and in what order. The course ends with a summary task where you capture what you’ve learned through the six completed tasks.

All courses within the Leadership for Sustainable Change package are designed to be flexible, assuming that it can be hard to leave work and come to campus at specific dates and times. As a student, you can complete the course assignments in your own order and at your own pace through a digital platform.

The course Leadership for Sustainable Change 2 is part of a package of four courses whose common purpose is to develop your practical and theoretical skills, enabling you to take on a role as a transformative agent and leader at your workplace and in society. The courses are launched gradually and are continuously developed based on your feedback and how the courses are received.

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

The course gives an introduction to hydrometallurgical principles, where students learn about chemical water-based systems and hydrometallurgical methods for metal extraction. Some environmental aspects that are crucial for sustainable metal extraction are highlighted.

The course is under development and is planned for January 2027.

The course is under development and is planned to start in the spring of 2027.

This course addresses the urgent need to transition metallurgical industries towards sustainable, carbon-free practices. Designed for industrial professionals and researchers, it provides comprehensive understanding of both environmental impacts and cutting-edge technological solutions transforming metal production.

The curriculum begins with the context and imperative for sustainable metallurgy within global climate frameworks. You will explore alternative reduction technologies, studying hydrogen-based processes, electrolysis, and innovative techniques while evaluating your technical feasibility and real-world applications.
The course examines sustainable energy integration challenges, focusing on renewable sources, storage technologies, and grid strategies essential for industrial implementation. Special attention is given to hydrogen’s revolutionary role in metallurgy, covering production methods, applications in metal processing, safety considerations, and infrastructure requirements.
Through a culminating entrepreneurial project, you will develop innovative solutions by forming interdisciplinary teams to address specific challenges, creating business plans and presentations while maintaining reflective learning journals. This transformative educational experience builds both theoretical knowledge and practical skills, enabling you to become an effective change agent driving the decarbonization of metallurgical processes—an essential step toward industry’s sustainable future.

Virtual commissioning is a technique used in automation and control engineering to simulate and test a system’s control software and hardware in a virtual environment before it’s physically implemented. The goal is to identify and fix any issues or errors in the system before it goes live, which reduces the risk of downtime, safety hazards, and costly rework.

The virtual commissioning process usually involves creating a digital twin of the system being developed, which is a virtual representation of the system that mirrors its physical behavior. The digital twin contains all the necessary models of the system’s components, such as sensors, actuators, controllers, and interfaces, as well as the control software that will run on the real system. Once the digital twin is created, it can be tested and optimized in a virtual environment to ensure that it behaves correctly under different conditions.

The benefits of virtual commissioning include reduced project costs, shorter development time, improved system quality and reliability, as well as increased safety for both operators and equipment. By identifying and resolving potential issues in the virtual environment, costly and time-consuming physical testing and troubleshooting can be avoided, which can significantly cut project costs and time to market.

Virtual commissioning is divided into two courses. This is the first course where you develop understanding and skills in the virtual commissioning process in the process and manufacturing industry.

Virtual commissioning is a technique used in automation and control engineering to simulate and test a system’s control software and hardware in a virtual environment before it’s physically implemented. The goal is to identify and fix any issues or errors in the system before it goes live, which reduces the risk of downtime, safety hazards, and costly rework.

The virtual commissioning process usually involves creating a digital twin of the system being developed, which is a virtual representation of the system that mirrors its physical behavior. The digital twin contains all the necessary models of the system’s components, such as sensors, actuators, controllers, and interfaces, as well as the control software that will run on the real system. Once the digital twin is created, it can be tested and optimized in a virtual environment to ensure that it behaves correctly under different conditions.

The benefits of virtual commissioning include reduced project costs, shorter development time, improved system quality and reliability, as well as increased safety for both operators and equipment. By identifying and resolving potential issues in the virtual environment, costly and time-consuming physical testing and troubleshooting can be avoided, which can significantly cut project costs and time to market.

Virtual commissioning is divided into two courses. This is course 2, where you develop understanding and skills in the virtual commissioning process, enabling engineers to test and validate control systems and production processes in a simulated environment before implementing them in real life.

Virtual commissioning is divided into two courses. This is course 2, where you build understanding and skills in the virtual commissioning process, letting engineers test and check control systems and production processes in a simulated setup before putting them into real life.

eXtended Reality (XR) for Green Transition explores how VR and AR technologies can accelerate sustainable development. The course starts with the concepts of reality, perception, and the mind, explaining why virtual experiences feel real. It even applies these ideas to other species. Students then study the reality-virtuality continuum, current graphics and tracking technology, and their implications for ethics, neuroplasticity, and cybersecurity. The course focuses on green applications: reducing travel, supporting Industry 5.0 design and simulation, optimizing industrial processes, and creating adaptive, immersive education. The course is delivered through pre-recorded lectures, self-study materials, and hands-on assignments, giving participants both theory and practical experience in building sustainable XR solutions.

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 fee 0 sek
Örebro University and Luleå University of Technology

This course highlights how intelligent sensor systems can contribute to increased sustainability and enable a green transition. Participants will gain basic knowledge of sensor technology and how sensor data can be integrated into intelligent, distributed systems. The focus is on applications in energy efficiency, environmental measurements and monitoring, as well as sustainable automation. The course covers sensor technology, embedded systems, distributed computing, resource-efficient machine learning methods, and federated learning for privacy-preserving and decentralized model training across sensor nodes. Through lectures, practical examples, and project work, participants gain experience in designing and developing prototypes of intelligent sensor systems tied to real-world, sustainability-related use cases.

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.

Kickoff Seminar 23.10. 9:15-12:00
1st Workshop 27.11. 9:15-12:00
2nd Workshop 11.12. 10:15-12:00

Start October 23rd, 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

High-performance computing (HPC) for computer vision focuses on leveraging advanced computing techniques to speed up intensive workloads. This course gives participants knowledge in parallel computing, GPU programming, and scalable algorithms specifically tailored for computer vision applications. The goal is to develop expertise in using HPC resources to efficiently solve complex vision problems. To this end, participants will gain basic knowledge of the HPC software stack, including CUDA, OpenVINO, and the programming language Mojo.

This course is designed for engineers, scientists, operators, and managers interested in utilizing AI-based methods for condition monitoring and prognostics in industrial systems and high-value assets. Participants will learn to identify common failure causes and predict Remaining Useful Life (RUL) using historical data, involving tasks such as data processing, feature selection, model development, and uncertainty quantification. Led by experienced professionals from industry and academia, the course covers the basics of prognostics and introduces various AI methods, including deep learning. It represents state-of-the-art AI-driven prognostic techniques, advanced signal processing, and feature engineering methods.

This course explores the integration of artificial intelligence (AI) in decision support systems specifically tailored for the energy and production sectors. You will learn how AI technologies, such as machine learning, optimization, and data analytics, are transforming traditional operational strategies, enhancing decision-making processes, and driving efficiency in energy and production operations.

The course covers foundational concepts of AI and decision support systems, along with practical applications such as predictive maintenance, demand forecasting, process optimization, and real-time decision support. Through hands-on projects, case studies, and industry-relevant examples, you will gain insight into designing and implementing AI-driven solutions that improve operational performance, reduce costs, and support sustainability goals.

By the end of the course, you will be equipped with the skills to develop and apply AI-driven decision support systems to solve complex challenges in energy and production environments. This course is ideal for professionals and students interested in leveraging AI for operational excellence in the energy and production industries.

This course emphasizes large language models (LLMs) as a key technology that can reshape how industries operate, communicate, and innovate. The course covers topics ranging from traditional natural language processing (NLP) to natural language understanding (NLU), while highlighting the use of LLMs to tackle industry-specific challenges with a focus on sustainability goals. Through hands-on examples and exercises, participants gain skills to apply LLMs to real-world problems while promoting sustainable development.

This course gives you a deeper understanding of the Internet of Things (IoT) and the related cybersecurity challenges. You’ll learn about the basics of IoT and its applications, the communication protocols used in IoT systems, the cybersecurity threats targeting IoT, and the measures that can be taken to counter these threats.

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

This course is designed for you who want to learn about functional safety of battery management systems and it is an introductory course. The course also covers other safety aspects such as fire safety in relation to rechargeable energy storage systems (RESS) and associated battery management systems.

About the course

During the course, you will develop skills in principles of battery management systems, functional safety and other safety aspects of safety such as fire safety, hazard identification, hazard analysis, and risk assessment in relation to battery management systems. The course also provides a broader understanding of the many different aspects of safety.

The course consists of two modules:

• Introduction to battery management systems, functional safety of battery management systems and principles of functional safety as well as other safety aspects such as fire safety.
• Risk identification, analysis, and assessment related to battery management systems.

The course is primarily aimed at engineers that need to ensure that battery management systems are safe, reliable, and compliant with industry standards. It is suitable for those with a background in functional safety, battery systems, the automotive industry or risk assessment.

The course consists of pre-recorded lectures and 1-3 scheduled online meetings.

You will learn

• Principles of battery management systems, functional safety, and other safety aspects such as fire safety.
• How to identify, analyze, and assess risks related to battery management systems.

Requirements

Below you find the entry requirements for the course. If you do not fulfill the requirements, you can get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etcetera.

Application information

You’ll find the entry requirements in the course description. After submitting your application, the next step is to submit documentation to demonstrate your eligibility for the course. Most academic credentials from Sweden are retrieved automatically. Wait a few days after submitting your application – if you still can’t see your academic credentials om My pages, please upload them.

If you have studied in another country, you must provide transcripts of your academic studies and of your English proficiency. Exactly what you need to submit and how, depends on several factors. You can read more on universityadmissions.se or antagning.se.

If the course requires work experience, you need to provide an employer’s certificate. You can download a template for employer’s certificate below.

No academic qualifications?

Many courses requires that you have previous academies studies, but we can validate work experience to determine whether you have the qualifications for the course.

If you don’t have the formal qualifications required, please send in a certificate of employment (current or previous) and a CV/Description of competence that describes your educational and professional background. Please include a short description of your work experience, not only the work title.

Use the CV/ Description of competence template below and fill in the information requested.

You can also use our template for Employers certificate if you like.