AI+X · Research and Pedagogy Master (2 years)

01 — About

Purpose and concept

The objective of the educational program is to prepare highly qualified scientific and engineering specialists in the development, deployment, and management of artificial intelligence systems, data analytics, and intelligent transformation of digital and industrial systems.

The educational program “7M06101 AI+X” is aimed at preparing specialists in the development, deployment, and application of artificial intelligence technologies to solve complex scientific and applied problems in the context of digital and industrial transformation.

Students acquire competencies in machine learning, data engineering, computer vision, intelligent systems architecture, MLOps, and digital infrastructure development.

The program combines fundamental academic training with practice-oriented and research-based learning focused on solving real industrial challenges.

Code and name	7M06101 AI+X (research and pedagogy)
Field of education	7M06 — Information and communication technologies
Duration	2 years · 6 trimesters of 10 weeks
Volume	120 academic credits
Level	Level 7 (ISCED / NQF / SQF)
Degree	Master of Technical Sciences
Final assessment	Master’s thesis defence

Admission requirements

02 — Tracks

Choose your specialisation

Track 1

Industrial AI and Smart Systems

AI for industry and intelligent infrastructure: predictive diagnostics, digital twins, IoT and Edge AI, optimisation of industrial and energy processes.

Industrial AI and predictive diagnostics
Digital twins
IoT and Edge AI in industry

Track 2

AI Systems Engineering and Security

Engineering of AI systems: architecture, MLOps, data engineering, distributed and cloud systems, integration and deployment, security and risk analysis.

AI systems architecture and MLOps
Advanced machine learning
AI systems integration and deployment

Track 3

AI for Unmanned Aerial Systems

AI for unmanned aerial systems: design and programming of intelligent UAVs, computer vision, swarm intelligence and cybersecurity.

Computer vision for UAVs
Swarm intelligence and multi-agent systems
UAS cybersecurity

03 — Curriculum

What the programme consists of

Select a course to read its description. Credits are academic credits; one credit equals 30 academic hours.

Core courses34 credits

Electives (tracks)30 credits

Industrial AIIndustrial Artificial Intelligence and Predictive DiagnosticsThe course aims to study AI methods for predictive diagnostics of industrial equipment. It covers predictive maintenance, anomaly detection, sensor analytics, time-series forecasting, SCADA/IoT data processing, TensorFlow, PyTorch, and methods for improving industrial system reliability.

Industrial AIDigital Twins and Intelligent Infrastructure SystemsThe course aims to study digital twin technologies for intelligent infrastructure systems. It covers simulation modeling, IoT integration, real-time monitoring, BIM/GIS integration, predictive analytics, virtual engineering models, and optimization of complex industrial and energy systems.

UASIntelligent Control Technologies for Unmanned Aerial SystemsThe course provides theoretical knowledge and practical competencies in intelligent control of Unmanned Aerial Systems (UAS). It covers flight dynamics, navigation and positioning technologies, mission planning, autonomous control algorithms, sensor integration, communication systems, operational safety, and professional UAV operator training. Particular emphasis is placed on the application of Artificial Intelligence techniques for developing intelligent autonomous flight control systems.

Industrial AIAI for Industrial Process Optimization and ControlThe course aims to study AI methods for industrial process control and optimization. It covers SCADA systems, Industrial IoT, reinforcement learning, predictive analytics, digital modeling, TensorFlow, PyTorch, automation approaches, monitoring, and techniques for improving operational efficiency.

AI SystemsAI Systems Architecture and MLOpsThe course aims to study AI system architecture design principles and MLOps practices in industrial environments. It covers Docker, Kubernetes, MLflow, FastAPI, CI/CD pipelines, microservice architectures, model monitoring, deployment automation, and ML system maintenance using AWS, Azure, and Google Cloud platforms.

UASArtificial Intelligence and Programming of Autonomous Unmanned SystemsThe course covers advanced software development technologies for autonomous unmanned systems. Students study Python and C++, Robot Operating System (ROS2), flight controller programming, computer vision, machine learning, deep learning, autonomous navigation, SLAM technologies, multi-agent intelligent systems, and generative Artificial Intelligence. The course aims to develop competencies in designing intelligent autonomous UAV software and AI-based decision-support systems.

AI SystemsData Engineering for AI SystemsThe course aims to develop skills in building data infrastructures for AI systems. It covers Apache Spark, Hadoop, Kafka, PostgreSQL, NoSQL databases, ETL/ELT pipelines, data lakes, data warehouses, stream processing, and distributed data integration for analytics and intelligent applications.

AI SystemsDistributed AI Systems and Cloud ComputingThe course aims to study distributed AI systems and cloud AI infrastructure design methods. It covers distributed computing, Kubernetes clusters, GPU computing, Apache Spark, cloud-native architectures, AWS SageMaker, Azure ML, orchestration, AI service scalability, and reliable intelligent system operations.

UASEngineering and Technical Support of Unmanned Aerial PlatformsThe course focuses on engineering principles of design, integration, operation, and technical support of unmanned aerial platforms. Topics include UAV structural design, electronic subsystems, flight controllers, power systems, payload integration, diagnostics, maintenance, repair, modernization, and lifecycle management. Modern digital engineering approaches and reliability assurance of unmanned aerial systems are also covered.

Industrial AIAdvanced Industrial Analytics and OptimizationThe course aims to provide advanced knowledge in industrial analytics and process optimization. It covers advanced analytics, time-series analysis, predictive maintenance, optimization algorithms, Apache Spark, Power BI, Python-based analytics, digital modeling, and efficiency improvement methods for industrial systems.

Industrial AIArtificial Intelligence in Smart Energy SystemsThe course aims to study AI applications in smart energy systems. It covers smart grid technologies, energy forecasting, load balancing, predictive analytics, digital energy systems, IoT sensors, intelligent energy management, and optimization of energy infrastructure.

UASCybersecurity of Unmanned Aerial SystemsThe course focuses on contemporary approaches to information and cybersecurity for Unmanned Aerial Systems. It covers secure communication protocols, cryptographic data protection, security of telemetry and command-and-control links, resilience of navigation systems, cyber threat detection and prevention, and the application of Artificial Intelligence techniques to enhance the cyber resilience of autonomous aerial platforms.

Industrial AIIoT and Edge AI in IndustryThe course aims to study IoT and Edge AI applications in industry. It covers edge computing, distributed sensors, MQTT, embedded AI, computer vision at the edge, real-time analytics, industrial IoT architectures, and integration of intelligent devices into industrial operations.

AI SystemsAdvanced Machine Learning for AI SystemsThe course aims to provide advanced knowledge of machine learning methods for AI systems. It covers deep learning, transformers, ensemble learning, reinforcement learning, TensorFlow, PyTorch, optimization algorithms, model evaluation, and techniques for improving intelligent model performance.

UASComputer Vision for Unmanned Aerial SystemsThe course focuses on modern computer vision and digital image processing techniques for Unmanned Aerial Systems. Topics include object detection and recognition, image segmentation, video analytics, photogrammetry, remote sensing data processing, and deep learning methods. Special emphasis is placed on the integration of computer vision algorithms into autonomous navigation and intelligent UAV control systems.

AI SystemsAdvanced MLOps and AI Lifecycle ManagementThe course aims to study advanced MLOps approaches and AI lifecycle management. It covers ML pipelines, Kubeflow, MLflow, CI/CD for ML, monitoring, drift detection, automated retraining, governance, and management of industrial AI system operations.

AI SystemsAI Systems Integration and DeploymentThe course aims to develop skills in integrating and deploying AI systems in corporate and industrial environments. It covers API integration, FastAPI, Docker deployment, cloud deployment, testing frameworks, enterprise architecture, DevOps practices, and operation of AI solutions in production environments.

UASSwarm Intelligence and Multi-Agent Systems for UAVsThe course explores the theoretical and practical foundations of collaborative operations in Unmanned Aerial Systems. It covers swarm intelligence algorithms, multi-agent systems, cooperative navigation, task allocation, inter-agent communication, distributed decision-making, and Artificial Intelligence methods for coordinating autonomous UAV teams

Practice, research work, final assessment56 credits

Structuring research results into a coherent and logically organized framework, including systematization of theoretical and empirical materials, justification of methodology, description of applied methods and models, analysis and interpretation of results, formulation of conclusions and recommendations. Preparing a master’s thesis in accordance with academic standards, including formatting of text, tables, figures, references, and appendices. Developing and delivering a scientific presentation with clear argumentation, visualization of results, and justification of scientific novelty and practical significance.

04 — Learning outcomes

What you will be able to do

LO1Develop strategies for organizations and complex systems in digital and industrial transformation contexts, apply system analysis and modeling techniques, make data-driven decisions, and manage technological change.
LO2Apply data collection, processing, storage, and analysis methods, and use analytics and visualization tools to solve complex technical and applied problems.
LO3Apply machine learning and AI methods, design, train, and optimize models, evaluate their performance, and use them to solve applied problems.
LO4Design AI system architectures, apply data engineering methods, and develop scalable high-load intelligent systems.
LO5Deploy AI models in production, manage their lifecycle using MLOps tools, perform monitoring, and ensure model reliability
LO6Apply AI technologies to optimize industrial processes, energy systems, and intelligent infrastructures, and develop efficiency-enhancing solutions.
LO7Design distributed and cloud-based AI systems, manage data infrastructures, and implement solutions in high-load environments.
LO8Develop digital products based on generative AI, define functionality, create prototypes, and deploy them in practice.
LO9Conduct research in AI, analyze results, and apply them in academic and industrial contexts.
LO10Apply software engineering, API integration, DevOps, and enterprise architecture approaches for developing and deploying AI systems in corporate and industrial environments.
LO11Apply Agile, Scrum, Lean Startup, and product management approaches in AI product development, innovation management, and digital transformation implementation.

05 — Careers

Where graduates work

The professional activity area of graduates includes organizations involved in the development and deployment of artificial intelligence systems, data processing and analytics, and the intelligent transformation of digital and industrial systems.

Graduates can work in IT companies, AI and data processing centers, industrial and energy enterprises, telecommunications organizations, government agencies, digital platforms and startups, as well as research and educational institutions.

Professions from the Atlas of New Professions: AI Technologist; Business/Working Process Transformation Specialist.

Data AnalystData EngineerMachine Learning EngineerArtificial Intelligence EngineerAI Systems ArchitectMLOps EngineerComputer Vision SpecialistData Processing and Analytics SpecialistAI Systems Deployment and Support SpecialistIndustrial and Digital Process Optimization SpecialistAI Project ManagerExpert in high-tech companiesResearcherUniversity LecturerHead of Department and Deputy Head of Department

06 — Key dates

Upcoming intakes

How to apply

Intake 1

Applications25 October — 7 November
Enrolment9 November — 13 November
Classes start16 November

Intake 2

Applications20 November — 5 December
Enrolment7 December — 11 December
Classes start14 December

Intake 3

Applications20 January — 5 February
Enrolment8 February — 12 February
Classes start15 February

Ready to step into an AI career?

Leave a request and the admissions office will help you choose a programme and an intake.