Mälardalen University

The main goal of this course is to teach you basic knowledge and skills in argumentation.You will be engaged in co-constructing evidence-based justifications as well as in analyzing existing justifications in search of argumentation fallacies. Individual work as well as group-based work will allow you to practice. You will analyze climate-related articles (published in scientific literature but also in the news) and will extract the implicit underlying arguments and provide their analysis.Ultimately, this course will help you to develop basic argumentative skills needed to critically join the debate in society on climate goals. Who is the course for?CLIMATE GOALS, ARGUMENTATION, EVIDENCE is aimed at anyone who is interested in moving the first steps into the argumentation domain with the purpose of joining the debate on climate goals.An engineer (but also a politician) is expected to have founded arguments before taking any (climate-related) action. A citizen is expected to have founded arguments before engaging and sustaining any climate-related political agenda. How is the course structured?The course is a 4-week course. Each week mainly focuses on a single Intended Learning Outcome.

In this course, you will learn how data analysis in virtual production can improve your organization's results! Data analytics in virtual production uses advanced techniques to collect, analyze and present data to improve production. This system is designed to help companies optimize their production and increase efficiency. By learning how to model, do scenario analysis and evaluate using industrial software, identify bottlenecks, and use AI methods and applications, s necessary to succeed with a full production analysis. The course is given with flexible start and study pace, but we recommend a study pace of 20 %, which means that the course takes about 8 calendar weeks.

Numerical models are used in every engineering task, from conceptual design to optimization, control, and diagnostics. As the process becomes more complex, data driven models are a powerful tool that allows to quantify relationships between available data and observations, which forms the basis for machine learning. Image recognition, spam filtering, and predictive analytics are some examples of how we can use data driven models. This course provides a simple introduction to fundamental techniques for dimensionality reduction, classification, and regression, which can be applied to all types of engineering problems.

Learn about digital twins and how they can be used in smart production! A digital twin is used to create a virtual model of a real production system. Among other things, it can be used to simulate how the product will be manufactured, how materials flow and how machines move. The course gives you knowledge of industrial digital twins and their application within the framework of smart production. The course is given with flexible start and study pace, but we recommend a study pace of 20%, which means that the course takes about 8 calendar weeks.

Hydrogen is a clean fuel, a versatile energy carrier, and seems to be the answer to the climate change challenge. Why is everyone talking about it, and how is it going to replace traditional fuels? This modularized course provides a comprehensive overview on hydrogen as an energy carrier, with focus on fuel cell as hydrogen conversion technology. Hydrogen production and storage and their role in decarbonization will be covered. Different fuel cell technologies will be analyzed and discussed to present benefits and challenges in the use of hydrogen for power production, urban mobility, aviation, transportation, residential sector and much more. The learners will be able to combine the available modules to create their personalized education based on their needs and get insights on where and when hydrogen can play a role in a carbon-free society.

Do you want to deepen your knowledge in Industrial Internet of Things? In this course, you will gain deeper knowledge and understanding of the Industrial Internet of Things (IIoT), platforms and cloud services used in manufacturing industries. You will learn to understand the use of IoT platforms and how to design and implement simple systems and how to create value by using IoT solutions within industrial systems. The course will provide you with practical and theoretical knowledge in IIoT, platforms and cloud services as well as in-depth knowledge in production, logistics and product development.

The information and communication technology (ICT) sector is responsible for approx. 1.8-2.8% of the global greenhouse gas (GHG) emissions in 2020, and software is both part of the problems and the solutions. Traditional software engineering principles and techniques do not consider the climate, environment, and sustainability aspects in building and using software for any purpose. We, software engineers, developers, researchers, climate scientists, and various other related stakeholders, need to think about how we can reduce the carbon footprint due to building and using software-intensive systems. Green and sustainable software engineering is an emerging concept that can help reduce the carbon footprint related to software. In this introductory course, we will introduce the concept of green and sustainable software engineering and the engineering process to build green and sustainable software. Topics Sustainable and green computing Sustainable and green software engineering Process Energy efficient computing Sustainability issues in Scientific computing You will learnBy the end of the course, you will be able to: analyze the green and sustainability issues in traditional software engineering, identify and incorporate key elements to be included in the software engineering process to make the software green and sustainable, and use techniques to make your software code energy efficient. Who is the course for?This course is designed for those who are software developers, managers and software related policy makers, or have knowledge about software development, and want to consider the green and sustainability aspects in their everyday life. Also, this course will be useful for computational scientists who build green software and want to know more about these aspects in software engineering. However, this is an introductory course, and it will show a path for life-long learning to build more in-depth knowledge in each concept introduced in this course.

Do you want to learn the basics of Industry 4.0, at your own pace, whenever you want? Then the MOOC (Massive Open Online Course) Introduction to Industry 4.0 is for you. You will learn basic terminology and theory while gaining insight and understanding of the fourth industrial revolution and how it affects us. The MOOC: Introduction to Industry 4.0 is part of MDU's investment in smart production. The course is divided into ten modules, each of which describes different technologies in Industry 4.0. We estimate that it will take about 40 hours to complete the course and it is in English. The MOOC can also give you eligibility to apply for these 3 university courses at Mälardalen University: Internet of things for industrial applications, 5 credits Simulation of production system, 5 credits Big data for industrial applications, 5 credits

The course has the objective to provide proficiency in cybersecurity analysis and design in industrial settings, with a special focus on smart factories and Industry 4.0. To that aim, you will learn about advanced cybersecurity concepts, methodologies and tools. You will also be able to apply your knowledge to case-studies of industrial relevance.

This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.

In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

AI systems are increasingly being integrated into various industrial processes, including manufacturing, logistics, and autonomous vehicles. Trustworthy AI ensures that these systems operate reliably, reducing the risk of accidents or costly errors.  Trustworthy AI helps companies comply with ethical standards and legal regulations. It ensures that AI systems do not discriminate against certain groups, violate privacy rights, or engage in other unethical behaviors. Trustworthy AI System course can support in the development of more advanced AI technologies, fostering research collaboration, and attracting talent.