Job Description - Temporary Research Assistant II – Machine Learning Engineer (R7501) (A&SS) (25000BU) Founded in 1989, Hong Kong Metropolitan University (HKMU) is a modern, vibrant and dynamic university. We tailor our professional programmes to adapt to market trends and meet industry needs, thus providing our students with quality professional education and clear career paths. Being the first University of Applied Sciences (UAS) in Hong Kong, we pledge to play a pioneering role in enhancing recognition of vocational and professional education and training, and nurturing talents with both applied skills and knowledge. As a faculty-driven, student-centred university in support of innovative teaching and learning, strategic research, and stakeholder outreach to provide maximum benefit to our communities, we conduct research that advances knowledge and enhances teaching, focusing on strategic areas, including digital humanities and literature, international business, gerontechnology, personalised care, smart city, open and innovative education, and bilingual learning and teaching. HKMU is becoming an ever more vital link in addressing and helping Hong Kong to solve many difficult challenges – as part of our involvement in, and commitment to, the ‘metropolis’ of Hong Kong. Our plans to expand into the Greater Bay Area (GBA) will also cultivate talent to serve Hong Kong and the wider metropolitan GBA. We are now looking for a suitable person to fill the following position in the
School of Arts and Social Sciences : Temporary Research Assistant II – Machine Learning Engineer (R7501) (A&SS) - (25000BU) Job
Temporary/Part-time R&D School/Unit
School of Arts and Social Sciences Closing Date
10/Mar/2025, 3:59:00 PM Major Duties and Responsibilities The appointees shall mainly assist the research team in a research project “Virtual Reality-based Driving Training System” (R7501). This position focuses on machine learning applications in computer vision, including tasks such as image segmentation, 3D reconstruction, and simulation-based research. The appointee will be responsible mainly for the following: Developing and implementing machine learning algorithms for computer vision applications; Conducting experiments related to image processing, object detection, and 3D reconstruction; Assisting the Principal Investigator/supervisors in preparing datasets, models, and experimental setups; Supporting the integration of machine learning solutions into research prototypes; Performing other ad-hoc research activities as instructed by the Principal Investigator. Candidates Candidates should possess the following qualifications, experience and competence: Holders of a Bachelor’s degree or above in Computer Science, Creative/Interactive Media, Engineering, or any related disciplines will be considered; Strong knowledge of machine learning techniques and application in computer vision, such as image segmentation, object recognition, and 3D reconstruction; Experience with programming languages such as Python and libraries like TensorFlow, PyTorch, or OpenCV is highly desirable; Basic understanding of research methodologies and data processing pipelines; Fresh graduates are welcome, and candidates with an interest in pursuing further studies (e.g., a research postgraduate degree) are encouraged to apply; Detail-oriented, hardworking, organized, responsible, self-motivated, able to meet deadlines, and capable of working as a team or independently; Excellent command of spoken and written English and Chinese. 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. Please indicate your expected salary. Please contact Dr. William Lai at 3120-2531 for enquiries about the post. 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.
#J-18808-Ljbffr