This short course introduces core Artificial Intelligence (AI) methods applied in engineering and industrial decision-making. The course focuses on optimization techniques, artificial neural networks, and multicriteria decision-making methods that are commonly used in real-world industrial problems.

 

Artificial Intelligence Driven Process Control Systems introduces students to the principles, methods, and practical challenges of controlling industrial and cyber-physical systems using both classical and artificial intelligence-based approaches. The course begins with the fundamentals of process control, feedback, sensors, system modelling, and PID regulation, and gradually extends these concepts toward adaptive control, fuzzy logic, machine learning, reinforcement learning, digital twins, and intelligent industrial automation.

The course emphasises the practical integration of AI into real control architectures rather than treating AI as a stand-alone technology. Students learn how data from sensors can be processed, interpreted, and used to support prediction, optimisation, fault detection, adaptive decision-making, and supervisory control. Special attention is given to implementation constraints, safety, explainability, lifecycle management, human oversight, and industrial deployment, preparing students to design and critically evaluate AI-driven control solutions for modern manufacturing, robotics, energy, and process-industry applications.

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This course is devoted to a relatively new area - data spaces, which is natural development of data lakes and other data management approaches and technologies introducing governance as a key to the AI-ready organisations. The governance is looked from different prospective, including privacy, security, transparency, monetisation and others. To add more technical and practical dimension to the course different EU initiatives are discussed and introduced for better understanding of the data spaces concepts an their implementations. 

Learn how real clinical domains generate and use medical data (imaging, lab results, ECG/EEG signals, pathology, and genomics) and how to process it responsibly for analysis and research.

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This short course provides participants with a practical and applied overview of industrial robotics in manufacturing. Over three sessions, the course covers the fundamentals of robot-based workplace design, the implementation of robotic operations (e.g., pick-and-place and grinding), and the use of ABB RobotStudio for virtual simulation and programming. Participants will gain hands-on experience in configuring robot work cells, programming robot movements, and optimizing robotic processes in a simulated environment.

This three-part short course introduces participants to the concepts and practical tools of production digitalization. The course provides hands-on experience with digital simulation, production performance evaluation, flexible systems design, and immersive VR technologies using the Visual Components software. Each module focuses on a key aspect of digital manufacturing and offers structured exercises in a simulated environment.

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The course provides an in-depth understanding of Robotic Process Automation (RPA), focusing on the design and use of RPA systems for structured business process automation. The course covers modern programming languages and management algorithms used in the development of RPA solutions. It examines methods and algorithms necessary for data management and process optimization, using intelligent system management methods. Theoretical exploration of programming libraries and development tools in the context of RPA is offered. The interaction of external input/output devices using system management and process automation methods is also discussed. The course analyzes data flow control methods. Through practical tasks, students are introduced to data processing methods, graph search algorithms, classification, optimization, machine learning, and big data processing methods, which are crucial for effective RPA implementation.

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