Ural Federal University

What you'll study

The programme emphasises practical, application‑focused training in contemporary AI methods combined with a research project. Core taught topics include machine learning foundations, deep learning architectures, natural language processing, computer vision, reinforcement learning and scalable data processing. Students also study algorithmic foundations, probabilistic modelling, optimisation methods, software engineering for AI and issues of ethics and safety in AI deployment.

Teaching typically mixes lectures, laboratory classes and project work. Practical components cover model development and evaluation, deployment workflows, use of popular frameworks and libraries, and working with large datasets. The programme concludes with an extended master's thesis or applied project, often carried out in collaboration with industrial partners or research groups.

• Machine Learning and Statistical Methods
• Deep Learning and Neural Networks
• Natural Language Processing and Speech Technologies
• Computer Vision and Image Analysis
• Reinforcement Learning and Decision Making
• Big Data Architectures and Distributed Computing
• Software Engineering for AI Systems and MLOps
• AI Ethics, Safety and Regulation
• Research Methods and Master's Thesis / Applied Project

Entry requirements

Applicants should hold a recognised bachelor's degree in computer science, applied mathematics, information technology, engineering, physics or a related quantitative discipline. Strong programming skills and prior exposure to algorithms, linear algebra, probability and statistics are expected. Practical experience with coding and data analysis is an advantage.

Application documentation normally includes an academic transcript, CV, a statement of purpose, and references. International applicants must demonstrate language proficiency in the language of instruction (Russian or English, depending on the route selected) and may be asked to attend an interview or pass an entrance test; specific requirements vary by admission cycle. Applicants proposing a research or industry project should outline their interests and any relevant experience.

Career prospects

Graduates are prepared for roles developing and deploying AI solutions across sectors such as software and internet companies, manufacturing, robotics, healthcare, finance, telecommunications and government. Typical job titles include machine learning engineer, data scientist, AI engineer, computer vision engineer, NLP specialist, research engineer and AI consultant. Graduates may also continue to doctoral study in AI, data science or related fields and pursue careers in academic or industrial research.

The programme’s practical emphasis and links with regional industry help graduates move directly into applied roles that require both strong technical ability and experience with productionising models and data systems.

Why study at Ural Federal University

Ural Federal University is one of the major research and educational centres in the Urals region, located in Yekaterinburg. The university offers access to experienced teaching staff with active research in AI, access to computing facilities and laboratories, and opportunities for collaborative projects with regional technology companies and research institutes. The urban and industrial setting provides a strong environment for applied AI work, including internships and applied projects.

Students benefit from multidisciplinary links across engineering, natural sciences and economics within the university, structured supervision for the master's project, and support services for international students. The programme is designed to balance foundational theory, hands‑on technical training and project experience to prepare graduates for immediate employment or further research in artificial intelligence.