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Curriculum
Programme Courses

A detailed overview of all courses by semester, based on the official Course Descriptions document (D3.4). Click on any course to read a brief description and follow the link to its full syllabus.

3

Semesters

18

Available courses

90

Total ECTS

01Introduction to Industry 4.0 and 5.0
Core technologies of Industry 4.0 and 5.0 — artificial intelligence, data science, Industrial Internet of Things (IIoT) and cloud computing. Understanding the evolution of industrial automation and real-world use cases in manufacturing environments.
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From regression, decision trees and Support Vector Machines (SVM) to recurrent neural networks and deep learning. Emphasis on feature selection techniques and ensemble algorithms applied to industrial datasets.

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IoT/IIoT architectures, protocols and devices, with a focus on the convergence of Information Technology (IT) and Operational Technology (OT). Industrial IoT applications in real-world manufacturing scenarios.

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Fundamental concepts and architectures of Cyber-Physical Systems (CPS), their relationship with automatic control, distributed real-time embedded systems, IIoT and digital twins within the Industry 4.0 framework.

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Creation and deployment of digital twins for modelling physical objects and processes, using photogrammetry, machine learning and large language models. Real-world use cases in Industry 4.0 applications.

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Extended Reality (XR) technologies and their relevance to Industry 4.0. Hands-on development of XR solutions for training, design and digital transformation of industrial processes across different sectors.

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Circular economy principles embedded within the Industry 4.0 framework — remanufacturing, industrial symbiosis, smart cities and the design of sustainable production chains. Practical skills for real-world circular economy applications.

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Fundamental NLU/NLG techniques and machine learning models for text analysis, with a focus on industrial applications such as document processing, automated reporting and conversational AI systems.

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01Industrial Cybersecurity

Cybersecurity principles for Industrial Control Systems (ICS) and Operational Technology (OT), from threat detection and vulnerability assessment to incident response and security of industrial protocols (Modbus, OPC UA).

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Signal processing methods, data compression, dimensionality reduction and optimisation techniques for large-scale industrial datasets. Spatial data indexing structures and iterative optimisation schemes.

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Integration of AI, IoT and data analytics to build a manufacturing environment that dynamically adapts to production and supply chain requirements. Principles of digital and innovative industrial transformation.

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Scientific methodology (research design and execution), technical writing, and project management. Use of AI tools (e.g. ChatGPT) for document drafting and project scenario simulation.

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Blockchain technology, cryptographic security and consensus mechanisms (Proof of Work, etc.), applied to improving transparency and integrity in industrial supply chains and distributed trusted systems.

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From digital image filtering and feature detection to Convolutional Neural Networks (CNN), depth estimation and object tracking in industrial production lines. Practical exercises in feature matching and image alignment.

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Principles of interactive systems design, usability evaluation methods and user interface development techniques, applied to real-world industrial information systems. From requirements gathering to prototype development.

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Penetration testing methodologies and incident response procedures for industrial networks (IT/OT convergence). Vulnerability discovery tools (Nmap, Metasploit, OpenVAS) and exploitation techniques targeting industrial protocols.

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