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Individual programmes

Engineering

Programme Overview

The Master of Science in Engineering in Robotics and Intelligent Systems programme aims to prepare students for a career in robotics engineering. The programme’s content includes mechanical design, programming, and integration skills for building, operating, and applying robotic systems. The programme also emphasises teamwork and collaboration, preparing students for the many tasks necessary to accomplish these goals. Upon graduation, students are well-prepared to become independent researchers and engineers, and many will go on to become leaders in robotics research in both academia and industry.

Key features/Highlights

Explore the foundations of robotic systems, including robot modelling, sensing, planning, control, intelligence, and integration in our comprehensive core curriculum.
Tailor your education to your interests with elective modules such as Human-Robot Interaction, Autonomous Vehicles, Healthcare Robots, Internet of Things, and Machine Learning.
Apply your skills and knowledge to a collaborative, industry-driven capstone project, addressing real-world challenges and developing innovative solutions.

Professional Recognition

The degree of MSc(Eng) in Industrial Engineering and Logistics Management (IELM) has been accredited by the Chartered Institute of Logistics and Transport (CILTHK) in Hong Kong for full exemption from the examination requirements leading to the Chartered Membership.

Entrance Requirements

To be eligible for admission to the Taught Postgraduate Curricula in the Faculty, applicants should normally possess a Bachelor’s degree of this University in a relevant field such as engineering, sciences, mathematics, statistics, business studies, or the equivalent ; or a qualification of equivalent standard from this University or another comparable institution accepted for this purpose.

In addition to satisfying the requirements for admission to the curriculum concerned, an applicant who is seeking admission on the basis of a qualification from a university or comparable institution outside Hong Kong of which the language of teaching and/or examination is not English is required to obtain the equalivant examination results. For details, please visit https://portal.hku.hk/tpg-admissions/applying/admission-requirements.

Career pathways

Representative Employers

Student Sharing

“I appreciate the journey in MDASC. You would find every fundamental tool need to engage in the era of AI. The learning curve is steep, but indeed rewarding. Looking back, I can feel how deeper comprehensions towards statistics, deep learning, and databases I gained from those days and nights. In the rapid-changing phase of AI, it is precious that MDASC provides well-designed courses with comprehensive views towards algorithms: from storage to analysis, from statistics to engineering, from history to frontline, and provide rich opportunities to engage in their future to drive research and applications. No matter what your career is, MDASC would be helpful for shaping you into knowledgeable and confident person in data and algorithms in this data-driven world.”

DU Shenghui (Class 2024, FT)
Data Scientist, Deloitte HK

Student Sharing

“I am grateful for the MDASC programme and its supportive professors. The programme’s comprehensive curriculum covered every facet of the data science journey, encompassing theoretical foundations and practical applications through coursework and projects. As a credit risk modeller in a consulting firm, the programme equipped me with a diverse range of hard skills essential for success in my field. It significantly enhanced my expertise in vital areas such as data visualisation, statistical inference, and state-of-the-art machine learning algorithms. These skills have empowered me to deliver in-depth analyses to clients, fostering my professional growth and achievements. The MDASC programme has played a pivotal role in developing the precise skill set required for navigating the dynamic landscape of data-driven industries.”

MOK Ho Fung Arthur (Class 2024, PT)
Manager, Ernst & Young Advisory Services Limited