Alla kurser

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.

Att ha goda förmågor inom utvecklingsarbete får allt större betydelse i yrkeslivet. Den här kursen ger dig möjlighet att utveckla kunskap och förmåga inom produkt-, produktions- och affärsutveckling samt relationen mellan dessa.

Färdigheter i cirkulära affärsmodeller blir allt viktigare i yrkeslivet. Den här kursen ger dig specialiserade kunskaper och färdigheter i att utforma och implementera cirkulära affärsstrategier.

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.

Seminars;

• Seminar 1: October 15 at 10.00-11.30
• Seminar 2: tbd on first seminar
• Seminar 3: tbd on first seminar
• Seminar 4: December 15th at 13.00-15.00

Practical informationEntry 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.

Kursen ger en introduktion till hur vätgas kan framställas på hållbart sätt, med fokus på vattenelektrolys och biomassaförgasning

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.

Kursen fokuserar på att utveckla självledarskap och öka din reflektiva förmåga och göra dig medveten om det lärande som finns i dina erfarenheter och bemästra din vidare personliga utveckling som transformativ aktör och ledare.

Kursen är uppbyggd kring sex avgränsade uppgifter som genomförs separat. Uppgifterna hämtas från en uppgiftsbank där du väljer vilka uppgifter du gör och i vilken ordning. Kursen avslutas med en sammanfattande uppgift där du samlar in vad du har lärt dig genom de sex genomförda uppgifterna.

Kursen ingår i ett paket om fyra kurser vars gemensamma syfte är att utveckla dina praktiska och teoretiska kompetenser som gör det möjligt för dig att ta en roll som transformativ agent och ledare på din arbetsplats och i samhället. Kurserna lanseras successivt och utvecklas kontinuerligt genom din återkoppling och kursernas mottagande.

Kurserna är utformade för att vara flexibla och du kan delta även om du jobbar. Du kan genomföra kursuppgifterna i egen ordning och i din egen takt i en digital plattform.

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

Kursen Ledarskap för Hållbar Förändring 2 – Bygga Handlingskompetenser (LHF2) är en kurs för dig som vill vara med och driva på industrins och samhällets förändring mot hållbarhet.

Kursen syftar till att utveckla din handlingskompetens – att gå från ord till handling, stärka din initiativkraft och driva på utvecklingen av din erfarenhetsbaserade kunskap i din roll som transformativ agent och ledare.

Kursen är uppbyggda kring sex avgränsade uppgifter som görs klart var för sig. Uppgifterna hämtas från en uppgiftsbank där du väljer vilka uppgifter du gör och i vilken ordning. Kursen avslutas med en summerande uppgift där du fångar upp vad du lärt dig genom de genomförda sex uppgifterna.

Samtliga kurser inom kurspaketet Ledarskap för Hållbar Förändring är utformade för att vara flexibla och utgår från att det kan vara svårt att lämna jobbet och komma till campus vid specifika datum och tider. Som student kan du därför genomföra kursens uppgifter i din egen ordning och i din egen takt över en digital plattform.

Kursen Ledarskap för Hållbar Förändring 2 ingår i ett paket om fyra kurser vars gemensamma syfte är att utveckla dina praktiska och teoretiska kompetenser som gör det möjligt för dig att ta en roll som transformativ agent och ledare på din arbetsplats och i samhället. Kurserna lanseras successivt och utvecklas kontinuerligt genom din återkoppling och kursernas mottagande.

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

Kursen ger en introduktion till hydrometallurgiska principer, där studenterna lär sig om kemiska vattenbaserade system och hydrometallurgiska metoder för metallutvinning. Vissa miljöaspekter som är avgörande för hållbar metallutvinning belyses.

Kursen är under utveckling och planeras för januari 2027.

Kursen är under utveckling och planeras för start våren 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.

Virtuell driftsättning är en teknik som används inom automations- och reglerteknik för att simulera och testa ett systems styrprogramvara och hårdvara i en virtuell miljö innan det implementeras fysiskt. Syftet är att identifiera och korrigera eventuella problem eller fel i systemet innan det tas i drift, vilket minskar risken för driftstopp, säkerhetsrisker och kostsamma omarbetningar.

Den virtuella driftsättningsprocessen innebär vanligtvis att man skapar en digital tvilling av det system som utvecklas, vilket är en virtuell representation av systemet som speglar dess fysiska beteende. Den digitala tvillingen innehåller alla nödvändiga modeller av systemets komponenter, såsom sensorer, ställdon, styrenheter och gränssnitt, samt den kontrollprogramvara som kommer att köras på det verkliga systemet. När den digitala tvillingen har skapats kan den testas och optimeras i en virtuell miljö för att säkerställa att den beter sig korrekt under olika förhållanden.

Fördelarna med virtuell driftsättning är bland annat minskade projektkostnader, kortare utvecklingstid, förbättrad systemkvalitet och tillförlitlighet samt ökad säkerhet för både operatörer och utrustning. Genom att upptäcka och lösa potentiella problem i den virtuella miljön kan kostsamma och tidskrävande fysiska tester och felsökning undvikas, vilket avsevärt kan minska projektkostnaderna och tiden till marknaden.

Vrtuell driftsättning är uppdelad i två kurser. Detta är den första kursen där du utvecklar förståelse och kompetens inom processen för virtuell driftsättning i process- och tillverkningsindustrin.

Virtuell driftsättning är en teknik som används inom automations- och reglerteknik för att simulera och testa ett systems styrprogramvara och hårdvara i en virtuell miljö innan det implementeras fysiskt. Syftet är att identifiera och korrigera eventuella problem eller fel i systemet innan det tas i drift, vilket minskar risken för driftstopp, säkerhetsrisker och kostsamma omarbetningar.

Den virtuella driftsättningsprocessen innebär vanligtvis att man skapar en digital tvilling av det system som utvecklas, vilket är en virtuell representation av systemet som speglar dess fysiska beteende. Den digitala tvillingen innehåller alla nödvändiga modeller av systemets komponenter, såsom sensorer, ställdon, styrenheter och gränssnitt, samt den kontrollprogramvara som kommer att köras på det verkliga systemet. När den digitala tvillingen har skapats kan den testas och optimeras i en virtuell miljö för att säkerställa att den beter sig korrekt under olika förhållanden.

Fördelarna med virtuell driftsättning är bland annat minskade projektkostnader, kortare utvecklingstid, förbättrad systemkvalitet och tillförlitlighet samt ökad säkerhet för både operatörer och utrustning. Genom att upptäcka och lösa potentiella problem i den virtuella miljön kan kostsamma och tidskrävande fysiska tester och felsökning undvikas, vilket avsevärt kan minska projektkostnaderna och tiden till marknaden.

Virtuell driftsättning är uppdelad i två kurser. Detta är kurs 2 där du utvecklar förståelse och kompetens inom processen för virtuell driftsättning, vilket gör det möjligt för ingenjörer att testa och validera styrsystem och produktionsprocesser i en simulerad miljö innan de implementeras i verkligheten.

Kursen innehåller grundkonceptet eMaintenance, Industrial AI, asset management, underhållsgrunderna och deras praktiska tillämpningar i industriella domäner.

Introduction eXtended reality (XR) for green transition

eXtended Reality (XR) for Green Transition undersöker hur VR- och AR-tekniker kan accelerera hållbar utveckling. Kursen börjar med begreppen verklighet, perception och sinne och förklarar varför virtuella upplevelser känns verkliga. Kursen tillämpar till och med dessa idéer på andra arter. Studenterna studerar sedan verklighets-virtualitetskontinuumet, aktuell grafik- och spårningsteknik, och deras konsekvenser för etik, neuroplasticitet och cybersäkerhet. Kursen fokuserar på gröna tillämpningar: minskat resande, stöd för Industry 5.0-design och -simulering, optimering av industriella processer och skapande av adaptiv, immersiv utbildning. Kursen levereras genom förinspelade föreläsningar, självstudiematerial och praktiska uppgifter, och ger deltagarna teori och praktisk erfarenhet av att bygga hållbara XR-lösningar.

Introduction Applied Reinforcement Learning for simulation-based optimisation – YouTube

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

Denna kursen belyser hur intelligenta sensorsystem kan bidra till ökad hållbarhet och möjliggöra grön omställning. Deltagarna får grundläggande kunskaper inom sensorteknik och hur sensordata kan integreras i intelligenta, distribuerade system. Fokus ligger på tillämpningar inom energieffektivisering, miljömätningar och miljöuppföljning, samt hållbar automation. Kursen behandlar sensorteknik, inbyggda system, distribuerad beräkning, resurssnåla maskininlärningsmetoder samt federerad inlärning för integritetsbevarande och decentraliserad modellträning över sensornoder. Genom föreläsningar, praktiska exempel och projektarbete får deltagarna erfarenhet av att designa och utveckla prototyper av intelligenta sensorsystem kopplade till verklighetsförankrade, hållbarhetsrelaterade användningsfall.

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

Introduction: Machine Learning for Geology

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

Högpresterande datoranvändning (HPC) för datorseende fokuserar på att utnyttja avancerade beräkningstekniker för att påskynda intensiva arbetsbelastningar. Denna kurs ger deltagarna kunskap om parallellberäkning, GPU-programmering och skalbara algoritmer specifikt anpassade för datorseendeapplikationer. Målet är att utveckla expertis i att använda HPC-resurser för att effektivt lösa komplexa visionproblem. För detta ändamål kommer deltagarna att få grundläggande kunskaper om HPC-programvarustacken, inklusive CUDA, OpenVINO och programmeringsspråket 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.

Denna kurs betonar storskaliga språkmodeller (LLM) som en nyckelteknologi som kan omforma hur industrier fungerar, kommunicerar och förnyar sig. Kursen behandlar ämnen som sträcker sig från traditionell naturlig språkbehandling (NLP) till naturlig språkförståelse (NLU), samtidigt som man betonar användningen av LLM:er för att lösa branschspecifika utmaningar med hållbarhetsmål i fokus. Genom praktiska exempel och övningar får deltagarna färdigheter att tillämpa LLM:er på verkliga problem samtidigt som man främjar hållbar utveckling.

Denna kurs ger dig en fördjupad insikt i Internet of Things (IoT) och de relaterade cybersäkerhetsutmaningarna. Du kommer att lära dig om grunderna inom IoT och dess tillämpningar, de kommunikationsprotokoll som används i IoT-system, de cybersäkerhetshot som riktas mot IoT, samt de åtgärder som kan vidtas för att motverka dessa hot.

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.