The PhD academic program Intelligent Systems trains researchers and developers of intelligent information systems and IoT who are capable of designing, implementing, and managing solutions based on artificial intelligence methods (data mining, machine learning, deep learning, neural networks). The program combines theoretical modeling of intelligent processes with experimental implementation (software prototypes and systems), focusing on current professional standards and IT-trends in Kazakhstan.

— Deep Learning Methods

— Theoretical Computer Engineering

— Current Issues in Forecasting.

— Analytical and research competency: Analysis and structuring of professional information, preparation of analytical reviews with well-grounded conclusions and recommendations (PC1)

— Competency in defining tasks and requirements for intelligent systems: Development of technical specifications, defining goals, efficiency criteria, and applicability constraints (PC2, PC7)

— Knowledge modeling and AI methods development competency: Building knowledge representation models, applying AI problem-solving techniques and knowledge engineering, developing methods for non-standard tasks (PC4, PC6)

— Design and development of intelligent systems competency: Creating new design/development methods, implementing human-computer interaction, solving optimization problems using AI algorithms (PC3, PC5)

— Strategic, forecasting, and innovation competency: Forecasting the development of intelligent systems and advanced information technologies, developing competitive ideas and research/implementation trends (PC8, PC9).

— Focus on advanced AI methods: in-depth study of ML/DL, neural networks, knowledge engineering, and data mining with an emphasis on developing new methods and models

— Practice in real “smart” environments: design and implementation of solutions for smart city/smart home/IoT, including monitoring and control

— Combination of "theory and experimentation": mathematical modeling of intelligent processes and mandatory software/prototype implementation

— Preparation for research and innovation: development of competencies for R&D, publications, patents, and implementation of intelligent technologies in industry

— High practical demand: skills applicable in healthcare, security, transport, fintech, industry, and other fields requiring autonomous and learning systems.

— Research Scientist/Researcher (AI/Intelligent Systems)

— Machine Learning/Deep Learning Engineer

— Intelligent Systems Architect/AI Solutions Architect

— Intelligent Systems and IoT Engineer (Smart City/Smart Home Engineer)

— Head of R&D/Tech Lead in Intelligent Systems.