PhD in
Robotics Engineering
Overview
The Master of Science in Robotics is a two-year, 120 ECTS, full-time program designed to prepare professional scientists, engineers and researchers for careers in the interdisciplinary field of robotics.
The program integrates robotics engineering, electrical and computer engineering, software engineering, control systems and artificial intelligence, providing students with an advanced understanding of modern robotic systems. Students progress from core knowledge in robotics, programming and control to specialized study and independent research.
Through advanced coursework, laboratory and project-based learning, research seminars, technical electives and a substantial master's thesis, students develop the skills needed to address complex robotics challenges in academia, research and industry. Graduates are prepared to contribute in Kazakhstan and internationally, particularly in technologically advanced and rapidly developing sectors.
The program integrates robotics engineering, electrical and computer engineering, software engineering, control systems and artificial intelligence, providing students with an advanced understanding of modern robotic systems. Students progress from core knowledge in robotics, programming and control to specialized study and independent research.
Through advanced coursework, laboratory and project-based learning, research seminars, technical electives and a substantial master's thesis, students develop the skills needed to address complex robotics challenges in academia, research and industry. Graduates are prepared to contribute in Kazakhstan and internationally, particularly in technologically advanced and rapidly developing sectors.
General information
- Campus: Astana, Kazakhstan
- Language: English
- Delivery mode: Full-time, on-campus
- Duration: 2 years
- Total ECTS credits: 240
Program Aims
- Preparing specialists for advanced careers in (national and multinational) industry and academia in Kazakhstan, in neighboring countries and internationally.
- Preparing future policy and decision-makers for careers in governmental agencies, public service, and the social economy, in Kazakhstan, and globally.
- Preparing entrepreneurs who will be able to promote advanced and innovative science-rich technologies anywhere in the world.
- Preparing a new generation of commercially aware and technologically advanced scientists and technologists to lead future developments in the chosen robotics engineering sub-discipline.
- Preparing future academics and researchers in robotics sub-disciplines for careers as specialists in universities of Kazakhstan and other parts of the world.
Key Advantages
- Research-Intensive Doctoral TrainingDevelop the expertise to design and conduct independent, innovative and original research in Robotics Engineering. After the first-year coursework, the program is primarily centered on individual doctoral research and the dissertation.1
- Research in Advanced RoboticsPursue research across emerging areas including intelligent systems, industrial automation, human–robot interaction, medical and rehabilitation robotics, as well as advanced control, robot perception, manipulation and autonomous systems.2
- Flexible Advanced ElectivesShape your doctoral studies around your research interests through advanced courses, with additional Level-8 courses available subject to supervisory approval.3
- Individual Multidisciplinary SupervisionWork with a dedicated Supervisory Committee consisting of a Lead Supervisor, an Internal Co-Supervisor and at least one External Co-Supervisor, providing complementary expertise throughout the development of your doctoral research.4
- International Research & Networking OpportunitiesDevelop your international research profile through collaboration, conferences and networking. PhD students may also undertake a 3–12 month research internship at an international host research facility, subject to funding and approval.5
- Publication & Academic Career DevelopmentBuild a strong scholarly profile through research publications, presentations, scientific communication and national and international networking. The program also provides experience relevant to academic careers, including participation in the University's teaching mission.6
Learning Outcomes
Upon successful completion of this program, students will be able to:
- Review, analyse, and evaluate the body of scientific literature in their field of study in Robotics Engineering.
- Develop and implement research methodologies in the field of Robotics Engineering.
- Plan, develop, analyse and communicate research in writing and verbally independently.
- Generate original knowledge and understanding in their area of specialization within Robotics Engineering.
- Judge technically and ethically and relate their research to the broader field of knowledge in their Robotics Engineering sub-discipline.
- Develop their academic/scholarly career through presentations, publications, and national and international networking.
What Will You Learn?
- Independent Research Design & MethodologyLearn to identify significant research problems, formulate research questions and hypotheses, design appropriate methodologies, conduct experiments, collect and analyse data, validate results and generate original knowledge in Robotics Engineering.
- Critical Analysis of Scientific LiteratureDevelop the ability to systematically review, evaluate and synthesize current research, compare methodologies and results, identify research gaps and contradictions, and position your work within the wider robotics research landscape.
- Robot Motion Planning, Dynamics & Advanced ControlExplore advanced approaches to robot motion planning, dynamic programming, numerical optimal control, model predictive control, state-space methods, adaptive control and estimation for robotic and mechatronic systems.
- Robot Perception & Intelligent SystemsStudy advanced approaches to computer vision, digital image processing, machine learning for robot perception, object recognition, pose estimation and multimodal sensory processing for intelligent robotic systems.
- Intelligent Robot ManipulationDevelop expertise in motion planning, robot grasping, classical and learning-based control, optimization, force and impedance control, and software solutions for robotic manipulation using technologies such as ROS 2.
- Scientific Communication, Publication & Academic DevelopmentLearn to communicate advanced research through scientific papers, presentations and a doctoral dissertation, evaluate research ethically and critically, participate in peer review and build an academic profile through publications and international networking.
Advanced Robotics Courses & Research Areas
Advanced Robot Motion Planning
Search-based and sampling-based planning, dynamic programming, optimal control, model predictive control and reinforcement-learning-related approaches.
Advanced Robot Kinematics
Mathematical modelling of serial and parallel manipulators, spatial transformations, direct and inverse kinematics and robotic workspaces.
Advanced Dynamic Systems & Control
Frequency- and state-space modelling, PID control, optimal control, model predictive control, adaptive control and advanced control algorithms.
Advanced Optimal Control & Estimation
Optimization, linear-quadratic control, disturbance observers and advanced control approaches for robotics and mechatronics.
Intelligent Robot Manipulation
Motion planning, grasping, learning-based manipulation, force and impedance control and ROS 2-based robotic systems.
Robot Perception & Vision
Computer vision, machine learning for perception, object recognition, pose estimation and processing of visual, audio and tactile information.
Research Areas
Intelligent Systems
Industrial Automation
Human–Robot Interaction
Medical & Rehabilitation Robotics
Robot Control & Motion Planning
Robot Perception, AI & Autonomous Systems
Curriculum
Electives
- ROBT 704 - Hardware / Software Co-Design
- ROBT 702 - Advanced Robot Perception and Vision
Core modules
The list of core modules
The course will facilitate the understanding of research planning and development, while enhancing the PhD student skills for reading, interpreting, writing and presenting his/her scientific and technical ideas to the peer community. The course will also deliver theory and examples of a variety of research methods and ethical principles associated to them.
Course learning outcomes
1. Identify the core responsible conduct of research topic areas.
2. Recognize the roles of professional ethics, integrity, responsibility and accountability.
3. Understand research profile.
4. Demonstrate effective scientific communication oration skills.
5. Understand how to conduct mentorship and collaboration with integrity.
Course learning outcomes
1. Identify the core responsible conduct of research topic areas.
2. Recognize the roles of professional ethics, integrity, responsibility and accountability.
3. Understand research profile.
4. Demonstrate effective scientific communication oration skills.
5. Understand how to conduct mentorship and collaboration with integrity.
After introducing the definition and applications of robot motion planning, the course provides a basic treatment of different algorithms, mainly focusing on offline planning. Methods including search-based planning, virtual potential fields, visibility maps, and sampling-based approaches are covered. The course then focuses on numerical optimal control and dynamic programming for robot motion planning. Approximate solutions to dynamic programming problems are also considered, along with their relationship to deep reinforcement learning. Finally, the course focuses on model predictive control for online motion planning problems. In addition to a general treatment of the theory underlying the described algorithms, the course discusses simple applications involving mobile robots and robot manipulators.
Course learning outcomes
1. Categorize, contrast and discuss motion planning algorithms for simple applications.
2. Assess the characteristics of algorithms based on dynamic programming.
3. Illustrate and discuss the fundamental properties of numerical optimal control and model predictive control algorithms.
Course learning outcomes
1. Categorize, contrast and discuss motion planning algorithms for simple applications.
2. Assess the characteristics of algorithms based on dynamic programming.
3. Illustrate and discuss the fundamental properties of numerical optimal control and model predictive control algorithms.
The course aims to introduce the doctoral students to the state-of-the-art analytical tools and methods used for control system design. The course will start with the modeling of physical systems in the frequency and state space domains. The student will master analytical tools such as Bode plots, root locus diagrams and phase plots. The second part of the course includes frequency domain-based control design (mainly focused on PID controllers) and then covers control system design in state space (linear quadratic regulators, servo-controllers, etc.). In the last part, students will glimpse different advanced control algorithms such as model predictive control, adaptive control and soft-computing-based control. The students will read and analyze advanced literature on the topic regularly.
Course learning outcomes
1. Understand the fundamental concepts of physical system modeling.
2. Apply fundamental frequency and state-space domain techniques in the analysis and design of linear feedback control systems.
3. Have a deep understanding of advanced control algorithms.
4. Implement control algorithms to simulation and real-world systems using software packages, toolboxes and relevant hardware.
Course learning outcomes
1. Understand the fundamental concepts of physical system modeling.
2. Apply fundamental frequency and state-space domain techniques in the analysis and design of linear feedback control systems.
3. Have a deep understanding of advanced control algorithms.
4. Implement control algorithms to simulation and real-world systems using software packages, toolboxes and relevant hardware.
This course aims to provide state-of-the-art concepts in optimal control, with applications to robot motion planning. First, a brief introduction to optimization is provided. Then, the course focuses on optimal control algorithms for deployment in practical robotics and mechatronics. Later, disturbance observers will be introduced to the dynamic systems with disturbance. The course covers linear-quadratic regulators and disturbance-observer-based control systems.
Course learning outcomes
1. Design linear optimal control algorithms.
2. Apply the methods to reference tracking problems in robotics and mechatronics applications.
3. Design linear disturbance observer and incorporate it to controller to improve the tracking performance.
Course learning outcomes
1. Design linear optimal control algorithms.
2. Apply the methods to reference tracking problems in robotics and mechatronics applications.
3. Design linear disturbance observer and incorporate it to controller to improve the tracking performance.
This course provides a comprehensive treatment of robotic kinematics, with emphasis on the mathematical modeling and analysis of serial and parallel manipulators. Core topics include spatial descriptions and transformations, homogeneous coordinates, Euler angles, and screw-axis representation for describing the position and orientation of rigid bodies. Methods for solving the direct and inverse kinematics of manipulators are presented, using both Denavit-Hartenberg convention and the method of Successive Screw Displacements. The analysis of manipulator workspaces, multiple inverse kinematic solutions, and the classification and position analysis of parallel manipulators are also addressed. Student projects apply these concepts to real robotic systems, typically involving the formulation and solution of the direct and inverse kinematics of industrial manipulators with either open or closed kinematic chains.
Course learning outcomes
1. Formalize the position and orientation of rigid bodies using appropriate spatial representations and transformations.
2. Formalize the direct kinematic model of serial and parallel manipulators.
3. Find the inverse kinematics solutions for open and closed kinematic chains.
Course learning outcomes
1. Formalize the position and orientation of rigid bodies using appropriate spatial representations and transformations.
2. Formalize the direct kinematic model of serial and parallel manipulators.
3. Find the inverse kinematics solutions for open and closed kinematic chains.
The course introduces modern principles of robot manipulation, including classical and learning-based robot control, motion planning and object grasping approaches. Topics include robot differential/inverse differential kinematics, optimization-based motion planning, manipulator statics and dynamics, joint and Cartesian position control, force and impedance control. Course project assignments focus on the implementation of robot control approaches and developing a complete object picking-and-placing software stack for a commercial robot-manipulator using state-of-the-art Robot Operating System 2 (ROS 2).
Course learning outcomes
1. Explain the core principles of intelligent robot manipulation, including motion planning, grasping, and control.
2. Analyze and present state-of-the-art research literature on learning-based robot manipulation.
3. Apply classical and learning-based approaches to robot manipulation control.
4. Develop ROS 2-based software solutions for motion planning and object manipulation.
5. Work effectively in a team to design, implement and evaluate a robot manipulation project.
Course learning outcomes
1. Explain the core principles of intelligent robot manipulation, including motion planning, grasping, and control.
2. Analyze and present state-of-the-art research literature on learning-based robot manipulation.
3. Apply classical and learning-based approaches to robot manipulation control.
4. Develop ROS 2-based software solutions for motion planning and object manipulation.
5. Work effectively in a team to design, implement and evaluate a robot manipulation project.
This course develops doctoral students' ability to read, evaluate, organize, and synthesize current research literature in a critical and methodologically transparent way. By the end of the course, students will be able to formulate a focused review question, design and document an appropriate literature search strategy, critically compare published studies, identify gaps and contradictions in the field of robotics engineering, and produce a concise survey paper.
Course learning outcomes
1. Define a focused review topic in robotics engineering and explain its significance.
2. Critically compare robotics research papers with respect to problem formulation, methodology, validation, assumptions, and limitations.
3. Write a concise survey paper that integrates and critically discusses recent literature in a robotics subfield.
4. Critically assess peer work and provide constructive, professional feedback.
Course learning outcomes
1. Define a focused review topic in robotics engineering and explain its significance.
2. Critically compare robotics research papers with respect to problem formulation, methodology, validation, assumptions, and limitations.
3. Write a concise survey paper that integrates and critically discusses recent literature in a robotics subfield.
4. Critically assess peer work and provide constructive, professional feedback.
ROBT 700 carries 210 ECTS, awarded upon successful defense of the thesis dissertation.
This course is designed to guide and assess the progress of PhD students as they engage in original, independent research at the doctoral level. It aims to foster the development of advanced research skills, critical thinking, and the ability to effectively communicate scientific findings. At the beginning of the semester, students will collaborate with their advisor to develop a comprehensive research plan. This plan will define a clear research problem, question, or hypothesis; provide relevant background and context; and outline a detailed research strategy, including experimental design, methods for data collection, analysis, and validation. Additionally, it will specify approaches for disseminating the results to the scientific community and beyond.
Course learning outcomes
1. Discover and pursue a unique topic of research in order to construct new knowledge.
2. Design and conduct an original research project.
3. Develop skills in designing a discipline specific research methodology.
4. Develop a working knowledge of relevant literature in the thesis discipline.
5. Practice academic writing appropriate to the discipline and learn how to participate in the peer review process.
6. Discuss research and other topics with academics in the thesis discipline.
This course is designed to guide and assess the progress of PhD students as they engage in original, independent research at the doctoral level. It aims to foster the development of advanced research skills, critical thinking, and the ability to effectively communicate scientific findings. At the beginning of the semester, students will collaborate with their advisor to develop a comprehensive research plan. This plan will define a clear research problem, question, or hypothesis; provide relevant background and context; and outline a detailed research strategy, including experimental design, methods for data collection, analysis, and validation. Additionally, it will specify approaches for disseminating the results to the scientific community and beyond.
Course learning outcomes
1. Discover and pursue a unique topic of research in order to construct new knowledge.
2. Design and conduct an original research project.
3. Develop skills in designing a discipline specific research methodology.
4. Develop a working knowledge of relevant literature in the thesis discipline.
5. Practice academic writing appropriate to the discipline and learn how to participate in the peer review process.
6. Discuss research and other topics with academics in the thesis discipline.
Elective Courses
The course introduces fundamental hardware and software concepts of computer systems as core elements of modern intelligent systems. Topics covered include basic microprocessor architectures, memory allocation, concurrency principles (multitasking, process synchronization), real-time systems, embedded and mechatronic system design. Course project assignments are related to the theoretical material covered in lectures, with a focus on developing practical skills in embedded system and GUI design using C/C++ programming languages in an embedded Linux operating system. The course will have a team-based final project requiring students to learn and apply topics and methods beyond the lecture material.
Course learning outcomes
1. Apply principles of embedded computing, hardware interfacing, and concurrent programming in computer-controlled technical systems.
2. Develop sensor and actuator interfaces on an embedded computing platform using appropriate communication, filtering, control and programming techniques.
3. Integrate sensing, computation, actuation, and control into a functional technical system prototype.
4. Communicate design concepts, technical decisions, experimental results, and system performance effectively in written reports and demonstrations.
5. Work effectively in teams to plan, develop, and deliver a complete engineering project.
Course learning outcomes
1. Apply principles of embedded computing, hardware interfacing, and concurrent programming in computer-controlled technical systems.
2. Develop sensor and actuator interfaces on an embedded computing platform using appropriate communication, filtering, control and programming techniques.
3. Integrate sensing, computation, actuation, and control into a functional technical system prototype.
4. Communicate design concepts, technical decisions, experimental results, and system performance effectively in written reports and demonstrations.
5. Work effectively in teams to plan, develop, and deliver a complete engineering project.
The course aims to introduce the doctoral students to the state-of-the-art of robot perception and vision.
Course learning outcomes
1. To acquire the fundamental concepts of a digital image processing and computer vision system.
2. To master the use of advanced machine learning techniques for robot perception.
3. To analyze complex signals to extract valuable information.
4. To develop object recognition and pose estimation methods for robots.
5. Describe and explain basic principles of audio, visual, tactile data processing with an appreciation for the real-world robot perception issues and constraints.
Course learning outcomes
1. To acquire the fundamental concepts of a digital image processing and computer vision system.
2. To master the use of advanced machine learning techniques for robot perception.
3. To analyze complex signals to extract valuable information.
4. To develop object recognition and pose estimation methods for robots.
5. Describe and explain basic principles of audio, visual, tactile data processing with an appreciation for the real-world robot perception issues and constraints.
Where do our graduates work?
- Career opportunities
- University Faculty / Assistant Professor
- Postdoctoral Researcher
- Robotics Research Scientist
- Senior Robotics / R&D Engineer
- Robotics Software Engineer
- Robot Control & Motion Planning Specialist
- Computer Vision / Robot Perception Researcher
- Autonomous & Intelligent Systems Researcher
- Product Developer / Advanced Technology Engineer
- Research & Innovation Specialist
- Technology Entrepreneur
- Technology Policy / Public-Sector Innovation Specialist
- Where?
- Universities & Higher Education
- Research Institutes & R&D Laboratories
- Robotics & Automation
- Artificial Intelligence & Intelligent Systems
- Advanced Manufacturing
- Autonomous Systems
- Medical & Rehabilitation Technologies
- Energy & Industrial Technologies
- Technology & Innovation Companies
- Government & Public-Sector Research Organizations
- Deep-Tech & Technology Start-ups
- National & Multinational Companies in Kazakhstan and Abroad
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53 Kabanbay Batyr Ave
Astana city, Republic of Kazakhstan
Astana city, Republic of Kazakhstan