DATA DRIVEN MODELING FOR ENGINEERS

Big data and the algorithms used in data science, together with the corresponding process and its technology tools, have important implications for addressing climate change. From machine learning algorithms to data visualization, data science methods are used to investigate and better understand climate change and its various effects on land, sea, food, etc.Data science is a powerful approach which is capable of helping practitioners, and policy-makers understand the uncertainties and ambiguities inherent in data, to identify interventions, strategies, and solutions that realize the benefits for humanity and the environment, and to evaluate the multiple– and sometimes conflicting–goals of decision-makers. In this MOOC course, we introduce methods pertaining to the growing field of data science and apply them to issues relevant to climate change. Topics Data science Analytics as a process Data-driven decisions Climate change Applications of data science in climate change Course content Understand data science Learn about the sources of big data Understand the basics of climate change, its impacts and sustainable development goals Get to know data-driven decisions and how they are made Highlight some climate change challenges that are directly or indirectly related to data science Apply data science knowledge and skills to make climate change related decisions Learn how others have used data science in association with addressing climate change problems You will learnBy the end of the course, you will be able to: obtain and analyze datasets; make data-driven decisions; identify and address climate change challenges using data science Who is the course for?This course is designed for those who want to improve their analytics and data-driven decision-making skills, with an emphasis on utilizing such skills for addressing climate change challenges. The course will also be useful for practitioners and policy-makers as they can benefit from understanding the uncertainties and ambiguities inherent in data and using it to identify interventions, strategies, and solutions that realize benefits for humanity and the environment.

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.

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.

This 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.

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

The course High-Performance Computing 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 November 18th 2024, 14:30 – 16:00  December 9th 2024, 14:30 – 16:00  December 20th, 2024, 14:30 – 16:00  January 13th, 2025, 14:00 – 16:00 Presentations of the tasks 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.