REEDEAM

This course has flexibel start and you may join between November 18 and December 9, 2024 This course explores the integration of artificial intelligence (AI) in decision support systems specifically tailored for the energy and production sectors. Students 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 curriculum will cover 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, participants will gain insights into designing and implementing AI-driven solutions that improve operational performance, reduce costs, and support sustainability goals. By the end of this course, students 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. You may join the course any time between November 18 and December 9, 2024. With the recommended study pace of 25%, the course would take approximately seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than February 22, 2025.

This course has flexibel start and you may join between October 21 and November 17, 2024 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. You may join the course any time between October 21 and November 17, 2024. With the recommended study pace of 25%, the course would take approximately seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than January 15, 2025. Scheduled online meetings November 11th 2024 January 15th 2025

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.

Business models that efficiently contribute to reduction of material use and waste are key to successful transition towards sustainability. This course has a particular focus on the interplay between business models, product innovation and production processes. Through this course, you will explore the critical relationship between sustainable practices and business strategies, preparing you to contribute meaningfully to the circular economy and sustainable development initiatives In this course, you will be introduced to systematic working methods for business development in practical contexts, with a specific focus on innovation and creativity. The goal of the course is to provide a deep understanding of the application of various business model practices in different types of development work. The objective is for course participants to enhance their ability to understand and apply business development processes in the manufacturing industry and gain deeper insights into how these processes relate to organizations' innovation and business strategies in order to achieve circular flows, resilience, and sustainability. The teaching consists of self-study using course literature, films, and other materials through an internet-based course platform, as well as scheduled webinars and written reflections. There are no physical meetings; only digital online seminars are included. Study hours 40 hours distributed from week 3, 2025 to week 8, 2025. Webinar 1: January 13thWebinar 2: January 20thWebinar 3: February 3rdWebinar 4: February 17th The first webinar is Januray 13th, but the course opens a week before, which means that you by then reach the course material and can start your training. Target GroupThis course is primarily intended for engineers in management or middle management positions within industry, whether they are recent graduates or individuals with extensive experience. The course is suitable for individuals with backgrounds in mechanical engineering, industrial engineering management, or similar educational background. Entry RequirementsTo be eligible for this course, participants must have completed courses equivalent to at least 120 credits, with a minimum of 90 entry Requirement credits in a technical subject area, with at least a passing grade, or equivalent knowledge. Proficiency in English is also required, equivalent to English Level 6. Educational package in circular economyThe course Product/production and business development for circular flows is an introduction of the educational package starting again spring 2024 and will also run spring 2026. This course: Business development for circular flow together with Product development for circular flows (starting March 3) and Production for cirkular flows (starting April 28) are free standing independent courses that build on knowledge in the field.

The Internet of Things (IoT) is a networking paradigm which enables different devices (from thermostats to autonomous vehicles) to collect valuable information and exchange it with other devices using different communications protocols over the Internet. This technology allows to analyse and correlate heterogeneous sources of information, extract valuable insights, and enable better decision processes. Although the IoT has the potential to revolutionise a variety of industries, such as healthcare, agriculture, transportation, and manufacturing, IoT devices also introduce new cybersecurity risks and challenges. In this course, the students will obtain an in-depth understanding of the Internet of Things (IoT) and the associated cybersecurity challenges. The course covers the fundamentals of IoT and its applications, the communication protocols used in IoT systems, the cybersecurity threats to IoT, and the countermeasures that can be deployed. The course is split in four main modules, described as follows: Understand and illustrate the basic concepts of the IoT paradigm and its applications Discern benefits and drawback of the most common IoT communication protocols Identify the cybersecurity threats associated with IoT systems Know and select the appropriate cybersecurity countermeasures Course Plan Module 1: Introduction to IoT Definition and characteristics of IoT IoT architecture and components Applications of IoT Module 2: Communication Protocols for IoT Overview of communication protocols used in IoT MQTT, CoAP, and HTTP protocols Advantages and disadvantages of each protocol Module 3: Security Threats to IoT Overview of cybersecurity threats associated with IoT Understanding the risks associated with IoT Malware, DDoS, and phishing attacks Specific vulnerabilities in IoT devices and networks Module 4: Securing IoT Devices and Networks Overview of security measures for IoT systems Network segmentation, access control, and encryption Best practices for securing IoT devices and networks Organisation and Examination Study hours: 80 hours distributed over 6 weeks Scehduled online seminars:  February 6th 2025, from 13:15 to 16:00 February 26th 2025, from 13:15 to 16:00 March 12th 2025, from 13:15 to 16:00 Examination, one of the following: Analysis and presentation of relevant manuscripts in the literature Bring your own problem (BYOP) and solution. For example, analyse the cybersecurity of the IoT network of your company and propose improvements The number of participants in the course is limited, so please hurry with your application!

This course has flexible start and you may join until December 8, 2024. The course is designed for you who wants to learn more about functional safety of battery management systems. The course will also cover other aspects of safety such as fire safety in relation to Rechargeable Energy Storage Systems (RESS) and associated battery management systems. In the course you will be able to develop skills in principles of Battery Management Systems, Functional Safety as well as of other aspects of safety such as Fire Safety, hazard identification, hazard analysis and risk assessment in relation to battery management systems. It also aims to provide a broader understanding of the multifaceted nature of safety. The course takes about 80 hours to complete and you can do it at your own pace. There are two scheduled meetings: One after five weeks to resolve any queries and another at the end of the course for the course evaluation. The date and time will be provided within a week of starting of course. Target GroupThis course is primarily intended for engineers that need to ensure that battery management systems are safe, reliable, and compliant with industry standards. The course is suitable for individuals with backgrounds in for example functional safety, battery systems, automotive or risk assessment. Entry requirements120 university credits of which at least 7.5 credits in software engineering and 7.5 credits in safety-critical systems engineering or 60 university credits in engineering/technology and at least 2 years of full-time professional experience from a relevant area within industry or working life experience regarding application of functional safety standards in the automotive domain or in other domains. The experience could be validated via a recommendation letter of a manager stating the involvement of the student in the development of functional safety artefacts. Proficiency in English is also required, equivalent to English Level 6.

The course High-performance Computer Vision in the Cloud provides participants with the necessary tools and skills to navigate large-scale computing infrastructures, emphasizing scalability and performance optimization. Large computing infrastructures can be the key to driving the industry’s green transition. The course recognizes the instrumental role of large computing infrastructures in facilitating a green industry transition, enabling industrial actors to reduce environmental impact and optimize resource utilization, aiming to minimize energy consumption. The course covers concepts such as enabling technologies (e.g., CUDA), distributed computing, multi-core architectures, hardware versus software acceleration, container solutions(e.g., Docker and Kubernetes), as well as metrics and tools for monitoring performance and memory management, providing participants with a comprehensive skill set to lead environmentally responsible solutions in the digital era.  Scheduled online seminars  January 27th, 14:00-15:30 February 7th, 14:00-15:30 February 17th, 14:00-15:30 February 28th, 14:00-16:00 Entry requirements At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" from the Swedish Upper Secondary School.    

In the era of shift towards green transition, industries face unique challenges and generates numerous opportunities. This course, "Intelligent Asset Management and Industrial AI" is designed to equip professionals with the knowledge and tools necessary to support advanced technologies in achieving environmental sustainability. Industries play a major role in contributing to the global economy that is accompanied with a significant share towards environmental degradation. The growing climatic concerns and degradation of natural resources has urged the need to reduce carbon footprints, minimize waste, and optimize resource utilization such that a green transition is achieved. Intelligent Asset Management and Industrial AI are at the forefront of this transformation offering innovative solutions to enhance operational efficiency, reduce environmental impact and support the industry’s commitment to sustainability. Furthermore, the course can help a professional to optimize the usage of resources, look for energy efficient systems, consider environmental changes, develop sustainable solutions, and integrate advanced technologies towards green transition. This is a problem-based course specific to an industrial sector. The problems can be provided by the course supervisor, or the participants can bring their own problems from their work. Common problems include e.g. asset management by balancing cost against performance, identifying, detecting, predicting, and planning for unexpected outages, disruptions or failures, exploring challenges and opportunities with AI and digitisation, monitoring the condition of industrial assets, and achieving sustainability goals. Target groupThe target group includes individuals working in various industries such as railway, mining, transportation, construction, manufacturing, logistics, energy, and other organizations that are or planning to implement asset management systems. This course can be suitable for professionals ranging from asset managers, maintenance and reliability professionals, operation managers, engineers, project managers, and asset management consultants. Online seminarsDecember 10th at 14.00 to 15.00January 14th at 14.00 to 15.00January 31st at 14.00 to 15.00February 13th at 14.00 to 15.00February 28th at 14.00 to 15.00 Entry requirements Bachelor’s degree of at least 180 ECTS or equivalent, which includes courses of at least 60 ECTS in for example one of the following areas: Maintenance Engineering, Mechanical Engineering, Materials Science, Data Science, Computer Engineering, Civil Engineering, Electrical and Electronics Engineering or equivalent. Or professional experience requirements four to five years of experience in relevant industries.

The course on Large Language Models for Industry is designed to cater to the demands of industries amidst the global push for sustainability and green transitions. Large Language Models (LLMs) represent a pivotal technology thatcan revolutionize how industries operate, communicate, and innovate. In this course, participants explore the intricate mechanics and practical applications of LLMs within industry contexts. The course covers the principles and technologies spanning from traditional Natural Language Processing (NLP) to Natural Language Understanding (NLU), enabled through the development of LLMs. Emphasizing industry-specific challenges and opportunities, participants learn to utilize LLMs while considering sustainability concerns. Participants gain valuable insights from adapting LLMs to tackle real-world problems through examples and exercises tailored to industry needs. By the course completion,participants are equipped to leverage LLMs as transformative tools for driving industry innovation and, at the same time, advancing sustainability goals. Scheduled online seminars November 14th 2024, 15:00 - 17:00 December 12th 2024, 15:00 - 17:00 January 9th 2025, 14:00 - 17:00 Entry requirements At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" from the Swedish Upper Secondary School.