6B06102 · GROUP B057 · BACHELOR’S
Physical AI
Intelligent physical systems
Intelligence that steps beyond the screen: it senses the environment, decides in real time and acts safely — on the shop floor, on the road and in the air.
3 years
DURATION
240 credits
PROGRAM WORKLOADWORKLOAD
English, Kazakh
LANGUAGE OF INSTRUCTIONLANGUAGE
Full-time, trimesters
MODE OF STUDYMODE
Bachelor in ICT
DEGREE
2 500 000 ₸ per year
TUITION
UNT 70 — fee-paying, 90 — grantUNT 70 / 90
PASS SCORE
20 June — 25 August
ADMISSION 2026–2027
ABOUT THE PROGRAM
If classical artificial intelligence lives in a browser, a chatbot or a recommendation feed, Physical AI works in the real physical world — on the shop floor, on the road, in the air and inside a production line.
This is the branch of AI that teaches machines to perceive their surroundings through sensors and cameras, process data in real time, read the situation, map the space, make decisions and act safely — with no right to “try again in a second”.
Hardware components and physical devices are studied only as far as is needed to understand how intelligent solutions work with sensors, actuators and the real environment. The main focus of the program is not designing hardware but building the intelligent software layer of physical systems.
CAREER
What graduates do
Graduates will be able to build intelligent robots, autonomous systems and AI solutions for industry, logistics, medicine, manufacturing and other sectors.
INDUSTRIES
FEATURES
Program features
The full technology stack — from model to device
The program does not stop at training a model “in a notebook”: you go from machine learning and deep learning, computer vision and generative AI all the way to Edge AI, MLOps and the industrial deployment of a solution.
A foundation that does not go out of date
Linear algebra, calculus, probability theory, statistics and optimization — the base that all modern AI engineering rests on, whichever frameworks come and go.
Flexible specialization within a single program
Six individual learning tracks — from robotics to smart city — let you choose the path that matches your own interests.
Communication at an international level
Professional communication in Kazakh, Russian and English is built into the structure of the program.
Responsible and safe AI as a standard
Digital ethics, cybersecurity, reliability and the principles of Responsible AI are studied systematically, not as an optional extra.
Technology entrepreneurship
The program teaches you not only to build solutions but also to assess their commercial potential — from prototype to product.
Practice at every stage of the program
Educational, professional and pre-diploma practice build real engineering experience long before graduation.
SPECIALIZATIONS
Educational tracks
The program offers individual learning tracks in the following areas.
Artificial intelligence for robotic and autonomous systems
Creating the intelligent “core” of robots and autonomous mobile platforms — from navigation algorithms to decision-making systems.
Edge artificial intelligence (Edge AI)
Building AI that runs fast and locally — right on the device, without constant dependence on the cloud.
Industrial artificial intelligence and smart manufacturing
AI solutions for smart factories and production lines that control processes in real time.
Sensor data processing and intelligent infrastructure
Working with data streams from industrial sensor networks and turning them into solutions for infrastructure systems.
Computer vision and machine perception
Building systems that recognize and interpret visual information in real time.
Distributed and multi-agent intelligent systems
Designing systems in which several intelligent agents work together within a single distributed architecture.
LEARNING OUTCOMES
What you will learn
On completing the program, a graduate is able to:
- Design the architecture of Physical AI software systems and choose software, computing and infrastructure solutions for autonomous, embedded and cyber-physical systems.
- Apply mathematical, statistical, computational and optimization methods to model physical processes and evaluate the quality of AI models.
- Develop software for intelligent systems using modern programming languages, algorithms, data structures and collaborative development tools.
- Design, train, optimize and evaluate models for machine learning and deep learning, computer vision, and generative and multimodal AI.
- Develop software for collecting, storing, transmitting, processing and analyzing the data of intelligent physical systems.
- Develop and optimize Embedded AI and Edge AI for intelligent devices and cyber-physical systems within real-time constraints.
- Design and deploy solutions for robotic, autonomous, industrial and infrastructure systems.
- Evaluate the reliability, safety, quality and performance of intelligent systems using the principles of Responsible AI and international cybersecurity standards.
- Carry out research and engineering experiments, build software prototypes of intelligent systems and justify the effectiveness of the proposed solutions scientifically.
- Communicate professionally in Kazakh, Russian and English and work effectively in interdisciplinary teams.
- Develop innovative software products and digital services in Physical AI, assess their commercial potential and apply the principles of technology entrepreneurship.
- Apply interdisciplinary knowledge — programming, engineering, mathematics, economics, entrepreneurship — to assess the technical, economic and social effectiveness of their own solutions.
PRACTICE AND TOOLS
Practice and tools
During their studies students gain hands-on experience with real industry tools and platforms.
Robotics, autonomous navigation, intelligent software for drones
Deploying and optimizing AI models on edge platforms
Digital twins and industrial integration
Intelligent urban infrastructure and smart city
Every core course ends with a concrete engineering result: an AI module for an autonomous drone, a software prototype of a smart city service, a digital twin of an intelligent system, an AI model deployed in industrial edge infrastructure.
THREE STAGES OF PRACTICE
Educational practice
Getting to know the tools of the industry
Professional practice
Working on real tasks
Pre-diploma practice
A portfolio of real projects by graduation
STUDY PLAN
What you study
Courses marked “Elective” are chosen by the student.
| Course Title | Credits |
|---|---|
| GENERAL EDUCATION DISCIPLINES | |
| Information and Communication Technologies | 5 |
| Foreign Language | 10 |
| History of Kazakhstan (State Exam) | 5 |
| Physical Education | 8 |
| Cultural Studies | 2 |
| Sociology | 2 |
| Political Science | 2 |
| Psychology | 2 |
| Kazakh (Russian) Language | 10 |
| Philosophy | 5 |
| Elective: Technological Entrepreneurship | 5 |
| Elective: Digital Entrepreneurship and Startups | 5 |
| Elective: Financial Literacy | 5 |
| FUNDAMENTAL DISCIPLINES | |
| Introduction to Programming | 5 |
| Fundamentals of Calculus | 5 |
| Object-Oriented Programming | 5 |
| Linear Algebra for Data Science | 5 |
| Discrete Mathematics for Computing | 5 |
| Applied Calculus | 5 |
| Educational Practice | 2 |
| AI Fundamentals | 4 |
| Algorithms and Data Structures for Data Science | 5 |
| Multivariable Calculus | 5 |
| Sensor Systems and Signal Processing | 5 |
| Academic Writing | 4 |
| Probability and Data Analysis | 5 |
| Databases | 4 |
| AI Systems Integration and Deployment | 5 |
| Fundamentals of Edge Intelligent Systems | 5 |
| Human-Machine Interaction | 4 |
| Real-Time Intelligent Systems | 4 |
| AI Ethics and Human-Centered Intelligent Systems | 4 |
| Elective: Autonomous Navigation Systems | 5 |
| Elective: Multi-Agent Systems | 5 |
| Elective: Edge AI for Robotics | 4 |
| Elective: Intelligent Control Systems | 5 |
| Elective: Sensor Networks and Industrial Data Processing | 5 |
| Elective: Smart Manufacturing and Industry 4.0 | 5 |
| Elective: Distributed intelligent systems | 4 |
| Elective: Intelligent Infrastructure Systems | 5 |
| MAJOR DISCIPLINES | |
| Machine Learning Systems | 5 |
| Computer Vision | 5 |
| Professional practice | 12 |
| Reinforcement Learning for Physical Systems | 5 |
| Pre-Diploma Practice | 4 |
| Industrial AI Platforms | 5 |
| Research Methods and Tools | 5 |
| Advanced Natural Language Processing | 5 |
| Deep Learning Systems | 5 |
| Simulation Methods in AI | 5 |
| Elective: AI for robotics and control systems | 5 |
| Elective: Algorithms of Autonomous Mobile Systems | 5 |
| Elective: AI for Drones and Unmanned Systems | 5 |
| Elective: Digital Twins and Intelligent Systems | 5 |
| Elective: Edge Artificial Intelligence and Model Deployment | 5 |
| Elective: Intelligent Urban Infrastructure Systems | 5 |
TUITION AND FUNDING
Tuition and grants
STATE GRANT
Covers the tuition in full
The national grant competition of the Republic of Kazakhstan: a qualifying UNT score, program group B057 “Information Technology”, the QPT and confirmed English.
QAIRU FOUNDER’S GRANT
100% of the tuition, awarded by competition
Three stages of selection: an online application, online selection and the final round at QAIRU. For finalists the university covers accommodation, meals and travel.
ADMISSION
How to apply
Take the UNT
70 points — fee-paying, 90 — the state grant competition. Program group B057 “Information Technology”.
Submit your documents
Registering at admission.qairu.kz takes a few minutes.
Confirm your language
English: IELTS from 5.0, TOEFL iBT from 65 or ITP from 460. Kazakh: KAZTEST at level B1.
Take the QAIRU Potential Test
Online, 35 minutes, 30 questions. The pass score is 23 out of 30.
| REQUIREMENT | VALUE |
|---|---|
| Study program | Physical AI |
| Language of instruction | English, Kazakh |
| Total UNT score | 70 |
| UNT score for the state grant competition | 90 |
| UNT scores in the core subjects | Mathematics — 5, Computer science — 5 |
| IELTS | 5.0 |
| TOEFL iBT | 65 |
| TOEFL ITP | 460 |
| Kazakh: KAZTEST / Qazaq Resmi Test | level B1 |
| Internal testing | QAIRU Potential Test: 35 minutes, 30 questions, pass score 23 |
Get a consultation
We will walk you through the program, the entrance tests and the payment — by phone or by email.
- 8 (700) 300 33 17
- info@nairu.edu.kz
Astana, 55/1 Mangilik El Ave., EXPO Business Center, block B 2.2
Mon–Fri, 09:00–18:00
QUESTIONS AND ANSWERS
Questions and answers
No. Hardware components are studied only as far as is needed to understand how intelligent solutions work with sensors, actuators and the real environment. The main focus of the program is the intelligent software layer of physical systems.
6B06102 · BACHELOR’S
Ready to apply?
Admission to the bachelor’s programs — closed. Registering at admission.qairu.kz takes a few minutes.
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