Basic subjects
Basics of programming
AI and Big Data analysis
Machine learning in Materials Science
Robotics in materials science
Specialized subjects
- Modeling of microfluidic chips and reactions in them
- Quantum chemical calculations
- Diffraction research methods
Neural networks in materials science problems
- Micro- and low-tonnage chemistry
- Generative artificial intelligence in the problems of materials science
Program Overview
Master's Degree program AI in Material Science represents a unique interdisciplinary program that combines advanced artificial intelligence methods with modern materials science. The program trains a new generation of specialists with good theoretical knowledge and practical skills in the field of physical methods used for materials research and capable of using machine learning technologies, neural networks and big data to solve problems of obtaining, automating the synthesis and research of new materials.
The training is based on a modular principle with a flexible specialization system through the selection of disciplines from thematic tracks. The first year lays the fundamental foundation. In the second year, students form an individual educational trajectory, choosing a specialization from two main tracks.
The robotics track is aimed at automating materials science processes — robotics and additive technologies in materials science, the development of devices and algorithms for automating chemical synthesis.
The AI data analysis and machine learning track includes working with databases of properties and various descriptors of materials, studying methods of visualization and interpretation of experimental data, deep learning and neural networks in materials science tasks, generative artificial intelligence for the design of new materials.
Program Benefits
- English-taught programme; Interdisciplinary approach: combination of courses in Physics, Chemistry and Data Science;
- Teaching by highly qualified professors engaged in various international research projects;
- Two tracks: 1) Robotics track; 2) AI data analysis and machine learning.
Prospects. Career & Employment
Graduates of the program can work as experts in academic and business institutions as a research scientist, computer chemist, or AI expert who uses artificial intelligence to advance chemical developments, materials development, and analytical methods.
Why is it important to pursue a higher education?
AI is important in materials science is an important field because it accelerates the discovery and design of new materials, predicts their properties, and streamlines research processes that are traditionally time-consuming and costly. By analyzing vast datasets, AI can identify optimal materials for specific applications, generate novel compounds, and enable a faster, more efficient cycle of hypothesis, prediction, and validation, which is crucial for innovation in fields like energy and manufacturing.