Automotive

Participants will first learn the basics of how generative AI works (LLM, Diffusion models, ...) and current examples of the tools. This will be followed by an overview and discussion of the legal background, examples of the advantages, risks, ethical aspects and abuses of the use of generative artificial intelligence.

Automotive

Participants will learn about basic data types, types of features for machine processing, basic principles of data acquisition and organization, databases, and the importance of data preprocessing and visualization.

Teacher: Laura AschbacherTeacher: Damjan Ekert

The innovation agent task force (in 2025 promoted to a working group) has been formed in the EU blueprint project FLAMENCO (Forward Looking Approaches for Green Mobility Ecosystem Network Collaboration) which acts as an expert panel to elaborate practical guidance for the parts of the ISO 560xx innovation management system norm series. In Q4/2024 a set of workshops started to elaborate the use of AI in ISO 56007 idea management and to document the experiences. This method to apply AI in such scenarios has been developed by the task force in 2024 for the strategic intelligence management part ISO 56006. 

The work of the innovation agent working group continued in the EU  project TRIREME and this MOOC course is about the results of the work on the ISO 56007 idea and opportunity management implementation using AI. This elaboration of best practices and training for the automotive industry is supported by the Erasmus+ blueprint project TRIREME (Digital & Green Skills Towards Future of the Mobility Ecosystem).

In future ideation is not limited to a group of experts it is open to an unlimited cloud of data and AI. In the pilot projects with industry , different AI models and experts were merged in the idea and opportunity management setup. 

Automotive

Building on fundamental principles of electrical safety and professional practice, this course focuses on the safe handling, operation, and risk management of battery systems in industrial environments. Batteries combine high energy density with reactive chemical materials, making them critical components that require a high level of technical awareness, discipline, and professional judgement.

Automotive

Duration: 2 hours (1 module, 4 units × 30 min)
Level: Awareness / Intermediate
Organisation: VSB – Technical University of Ostrava

This course introduces how Artificial Intelligence (AI) is transforming the software development lifecycle (SDLC). It covers practical applications of AI in requirements engineering, design, implementation, testing, deployment, and operations, along with key risks and limitations.

Learning Outcomes

After completing the course, learners will be able to:

• Explain AI applications across SDLC phases
• Use AI tools for basic software development tasks
• Identify benefits and limitations of AI in engineering
• Recognise key risks (e.g., security, hallucinations)
• Critically evaluate AI-generated outputs

Structure

Module: AI Across the SDLC (2 hours)

• Unit 1: Introduction & AI context
• Unit 2: Requirements & Design
• Unit 3: Implementation & Testing
• Unit 4: DevOps, Operations & Risks

Each unit (30 min) includes content, multimedia, and a short assessment.

Automotive

The course introduces the mathematical foundations of predictive maintenance and the basics of data analysis used in failure prediction. It also presents computer software and its configuration, along with the role of parallel computing in improving prediction efficiency.

Automotive

The course presents classic predictive maintenance techniques and introduces traditional predictive algorithms and methods used to detect potential failures. It also explores modern approaches and future directions, including the use of machine learning and advanced algorithms for more accurate prediction.

Automotive

The course presents the main types of machine failures and explains how different materials and operating conditions influence equipment performance. It introduces the basic concepts of reliability theory and discusses key reliability KPIs used in modern maintenance to evaluate and improve system performance.

Automotive

The course introduces the concept of predictive maintenance and explains its basic principles. It also compares predictive maintenance with preventive maintenance, highlighting key differences in maintenance planning and failure prevention. The course presents technologies used in predictive maintenance, including sensors, data analysis, and monitoring systems, as well as software that supports failure prediction. Examples of applications in various industries are also included.

Automotive

The objective of this course is to familiarize students with the fundamental concepts and essential procedures of quality assurance, quality management, and reliability within the automotive industry. The course introduces the most important standardized methods used for testing electronic materials and components. It also examines common failure mechanisms and potential protection strategies. Finally, several case studies are presented to illustrate practical applications of the discussed principles.

Automotive

This course builds on foundational knowledge to explore advanced industrial sensors and their integration into automation systems. Covering proximity, position, force/torque sensors, and electrical connections, learners will develop the ability to choose, configure, and evaluate sensors in dynamic and precision scenarios, gaining practical skills for high-performance, safe, and intelligent industrial applications.

Automotive

This course offers a practical, structured approach to the fundamentals of industrial sensing, combining core principles with real-world examples and hands-on applications. Focusing on temperature, pressure, level, and flow sensors, learners will explore how these devices operate, compare technologies, and select the most suitable sensors for various industrial processes, building the skills needed to monitor and optimise automation systems effectively.

Automotive

Modern industry relies on electrical automation to drive productivity, ensure safety, and maintain efficiency across numerous processes. This course provides a hands-on introduction to the core principles of industrial automation, focusing on the interpretation and application of control logic using real-world electromechanical systems.

Automotive

Welcome to Electricity Fundamentals, a foundational course designed for learners who want to have a basic understanding of electricity before diving into more advanced technical subjects.

Automotive

Change is the only constant in today’s business ecosystem. The ability to forecast, administer, and execute change is a massive competitive differentiator. This Change Management course attempts to provide a practical, structured framework for managing organizational transformation by integrating accepted theories with practical tools for implementation.

Teacher: Laura AschbacherTeacher: Liselotte AschbacherTeacher: DRIVES Learning Platform Dr. Richard MessnarzTeacher: Damjan EkertTeacher: Christian Vilanek

• Step 1: Using Gen AI for Data Analysis and Acquisition
• A set of sub-steps to use a specific analysis pattern  and refine sub-questions
• Step 2: Adding Evaluation Criteria
• Step 3: Automatic analysis of trusted source criteria
• Step 4: Review and Selection of Studies and Answers
• Step 5: Extract and present the results to the management

Automotive

This module, developed within the Digital & Green Skills Towards the Future of the Mobility Ecosystem (Trireme) project, explores Environmental Competence for Managers, a crucial capability for modern businesses. It highlights integrating environmental considerations into corporate strategies to enhance sustainability, regulatory compliance, and competitive advantage.

The module examines key components of environmental competence, including knowledge of environmental policies, sustainable resource management, ethical responsibilities, and leadership approaches in environmental decision-making. It underscores the role of competence-based management theories in fostering sustainability and emphasises the integration of ESG (Environmental, Social, and Governance) principles and circular economy strategies into business models.

Challenges in developing environmental competence, such as resistance to change, financial constraints, and lack of expertise, are discussed alongside case studies of corporate environmental failures, including Volkswagen’s emissions scandal and BP’s Deepwater Horizon spill. The document also presents emerging trends, including ESG-driven corporate governance, technological advancements in sustainability, and the growing importance of circular economy principles.

The module concludes with strategic recommendations for business leaders, advocating for mandatory ESG training, adopting AI-driven sustainability analytics, integrating circular economy models, and strengthening regulatory compliance frameworks. By implementing these measures, organisations can enhance resilience, foster innovation, and maintain long-term competitiveness in an increasingly eco-conscious business landscape.

Automotive

The course covers knowledge about materials and technologies relating to electronics technology, including, but not limited to, properties and types of electronics components, manufacturing of printed wire boards (unpopulated boards), assembling (Printed circuit Boards (PCB) - populated), design of PCB’s, also from the sustainability point of view. The course also overviews the other electronics manufacturing technologies, such as thin- and thick-film electronics. Furthermore, the course covers also basic knowledge of smart and green electronic trends and value chain aspects.

Automotive

This course gives a brief overview of Automotive SPICE, its structure, application and impact on the organisation.

Teacher: DRIVES Learning Platform Dr. Richard Messnarz

Gain the knowledge on how to collect special data from machines and rate their condition based on it.