Full-time
Type of study
English
Language
2 years
Duration
Rostov-on-Don location
Academic mobility option
Further studies at Ph.D. level
Study places available
5
State-funded places
20
Tuition fee-based places
Program description
This program is a unique interdisciplinary educational product for southern Russia, training a new generation of specialists—research engineers with knowledge and skills in applying AI-based algorithms.
Prospects. Career & Employment
  • Research organizations: Leading Russian and international research centers (e.g., the Kurchatov Institute, RAS institutes) and laboratories, including synchrotron centers (KISI, ESRF, ALBA, etc.).
  • High-tech industries: Chemical and oil and gas industries (catalyst development), aircraft and space industries (new materials), automotive, energy, and pharmaceuticals.
  • IT companies and data science: Data scientists, ML engineers, and computer vision specialists in projects related to science and technological processes (ScienсeX).
  • Academic career: Admission to graduate school at SFedU (based at the International Research Institute of Intelligent Materials, the Physics or Chemistry departments) and subsequent work as an independent researcher.

Top reasons to study

  • Relevance and Demand:
    The program is capable of training specialists to solve ambitious problems in materials science, including those needed to ensure the country's technological sovereignty, where the development of new materials is critical.
  • Modern infrastructure:
    Access to unique high-tech equipment, including the instrumentation of the International Research Institute of Intelligent Materials, the SFedU Shared Use Centers, and the Kurchatov Synchrotron Radiation Source (KISI) facilities.
  • Practice-oriented approach: Training is built around solving real-world scientific and industrial problems. Students work with relevant experimental data and apply AI to automate the synthesis and diagnostics of materials.
  • Interdisciplinarity:
    Synthesis of fundamental knowledge (physics, chemistry) with advanced IT technologies (machine learning, neural networks, Big Data, generative AI). Graduates acquire competencies that enable them to work effectively at the intersection of various scientific fields.
Core subjects
Basic disciplines
  • Physical aspects of materials science
  • Chemical aspects of materials science
  • Statistical approaches to data analysis
  • Supercomputers and computing clusters for data analysis and visualization
  • Quantum chemistry methods and computer-aided materials design
  • X-ray methods in materials science
Specialized disciplines
  • Chemoinformatics and Artificial Intelligence in Materials Science
  • Synthesis and Chemistry of Nanoporous Functional Materials
  • Additive Manufacturing and 3D Prototyping
  • Microfluidic and Flow Methods of Material Synthesis
  • Deep Learning and Generative AI for Scientific Problems
  • Intelligent Control Systems Architecture
Ways to enter SFEDU
See the application guide
Contact us
Fill in the request form and we will provide you with all necessary information