Academic Faculty Position — Electromechanics

About the position

Applicants are invited to indicate their preferred title in the application (e.g., Assistant Professor, Associate Professor (Dotsent), or Professor — or equivalent). The final position level will be confirmed by the University's Selection Committee based on the assessment of the candidate's qualifications, publications, and experience.

Responsibilities

• Teaching courses in electromechanics, electrical machines, electric drives, automated control systems, installation and commissioning of electrical equipment, relay protection and automation, at the undergraduate and graduate levels.
• Developing and updating course syllabi and instructional materials to reflect current requirements of digital industry and energy, including Industry 4.0, Industrial IoT, digital twins, Predictive Maintenance, and Energy Data Analytics.
• Delivering current applied courses on PLC/SCADA, intelligent electric drives, industrial networks and protocols, digital modeling of electromechanical facilities, equipment condition monitoring, and intelligent management of power systems.
• Supervising term papers, theses, and master's projects on electric drives, industrial automation, IIoT, digital twins, predictive maintenance, energy data management, and sustainable energy.
• Participating in the university's research and applied projects together with energy, industrial automation, water management, and industrial companies of the region.

Requirements

Education Requirements

• Possession of a PhD degree or an equivalent degree in electromechanics, electrical engineering, electric power engineering, automation and control, electric drives, mechatronics, or related engineering fields.
• A related specialization may be accepted provided the dissertation topic, publications, and professional experience are directly related to electromechanical systems, automation, industrial automatics, IIoT, digital modeling, intelligent control, or energy analytics.
• Ability to translate these competencies into high‑quality teaching and student supervision at all levels of higher education

Experience Requirements

• Academic and teaching experience in accordance with the requirements for academic teaching staff positions established by the university's internal regulations and the legislation of the Republic of Kazakhstan.

Language Requirements

English language proficiency is desirable.

Advantages

Core Competencies

• In-depth understanding of the operating principles of electromechanical systems, electric drives, electrical machines, electrical engineering calculations, and the installation, commissioning, and operation of electrical equipment; ability to teach relevant disciplines at a current methodological level.
• Expertise in automated process control systems, relay protection and automation, and operation of industrial and power electrical equipment, with the ability to train students in the relevant calculation and design methods.

Additional Competencies

• Industrial Automation: PLC, SCADA, HMI, variable-frequency drives, industrial control systems, programming and commissioning in Siemens TIA Portal, WinCC, Schneider EcoStruxure, and similar environments.
• Industrial IoT: OPC UA, MQTT, Modbus, industrial networks, smart sensors, data collection and transmission for monitoring and controlling electromechanical systems.
• Digital Twin in Electromechanics: modeling and developing digital twins of electric drives, electromechanical assemblies, and process equipment using MATLAB/Simulink, Ansys Twin Builder, Siemens Digital Twin, or similar platforms.
• Predictive Maintenance: vibration diagnostics, condition monitoring, analysis of equipment operating modes, machine learning, and failure forecasting to improve the reliability of electromechanical systems.
• Smart Drives and Intelligent Control Systems: digital electric drives, intelligent control systems, mechatronic complexes, integration of drive technology with automated production systems.
• Energy Data Analytics: using Python, SQL, Power BI, and other tools to analyze energy and operational data, visualize KPIs, and support data-driven decision-making.
• Smart Grid and Distributed Energy: basic understanding of smart grids, distributed energy, load management, and renewable energy integration.
• ESG Energy: energy efficiency, decarbonization, sustainable development of energy facilities, and understanding of ESG indicators for energy and industry.

Selection process

selection process includes interview, presentation of the teaching course and research work to be conducted

Documents

• a CV including a list of publications;
• a motivation letter (maximum 1 page) describing research and teaching interests;
• copies of diplomas and degree certificates;
• where applicable, a list of research projects and grants, as well as information on supervision of master's and PhD students.