We are now looking for a suitable person to fill the following position in the School of Science and Technology:

Temporary Research Assistant II (R6855) (S&T) (Ref: 26000M9)

Major Duties and Responsibilities

The appointee shall mainly assist the research team in a research project "Intelligent and Reconfigurable Rehabilitative Device Based on Multi-modal Fusion and Advanced Neural Network" (R6855). The appointee shall mainly be responsible for the following:

• Algorithm Implementation: Developing and optimizing deep learning models for multimodal sensor fusion and rapid, user-specific adaptation;

• Hardware Integration: Collaborating on the assembly and testing of a reconfigurable, 3D-printed prosthetic system;

• Experimental Trials: Coordinating and conducting trials with human subjects to evaluate system performance, repeatability and user satisfaction;

• Documentation: Assisting in writing technical reports, patent applications, and manuscripts for journals; and

• Conducting other research activities as instructed by the Principal Investigator.

Candidates

Candidates should possess the following qualifications, experience, and competence:

• A Bachelor's degree or above, preferably a Master’s or PhD degree, in Biomedical Engineering, Robotics, Computer Science, Electrical Engineering or a related discipline;

• Strong proficiency in Python and experience with deep learning libraries (e.g. PyTorch, TensorFlow);

• Practical experience in bio-signal processing or sensor fusion techniques;

• Familiarity with 3D printing (CAD) and embedded systems (e.g. Arduino, STM32, or similar) is highly preferred;

• Strong analytical thinking with ability to troubleshoot complex systems involving both software and hardware;

• Fluency in English and Mandarin, with strong English writing skills; and

• Proactive and able to navigate the "trial and error" nature of research.

Terms and Conditions for Appointment

Successful candidate will be appointed on a temporary full-time contract. Benefits will be provided in accordance with the statutory provisions.

To Apply

Candidates who are interested in joining us may submit their applications via the University’s eRecruitment System.

https://hkmu.taleo.net/careersection/ex_non_full_time/jobapply.ftl?lang=en&job=26000M9

Closing date of application:  28 April 2026

The personal data collected will be used for the purpose of considering your application for employment.  For details, please refer to the “Personal Data (Privacy) Notice for Job Applicant” on the University’s website. If you are not contacted by the University within eight weeks from the closing date of application, you may assume that your application was unsuccessful.