Full-time/Temporary Research Assistant I (Project No.: R7504) (E&L) at Open University of Hong Kong

, Hong Kong, China -

Full Time

Start Date

Immediate

Expiry Date

21 Jun, 26

Salary

0.0

Posted On

23 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Collection, Data Analysis, Database Compilation, Language Model Building, Machine Learning, Deep Learning, Large Language Models, Python, Programming Languages

Industry

Higher Education

Description

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. For more information about the University, please visit https://www.hkmu.edu.hk We are now looking for a suitable person to fill the following position in the School of Education and Languages: Major Duties and Responsibilities The appointee will assist the Principal Investigator for the Research Project “A Linguistic Method of Detecting Intoxication of Motorists for Ease of Fleet Driving Safety Management”. The appointee will be responsible mainly for the following: Assisting the research team with data collection and analysis; Supporting the research team in compiling a digitalized database; Supporting the research team in building a language model and other technical matters; Implementing and deploying advanced machine learning models (e.g. deep learning and large language models); and Expert-level proficiency in Python (including ML libraries) or other programming languages. Candidates Candidates should possess the following qualifications, experience, and competence: Hold a Master’s degree in computing, artificial intelligence or related fields; A positive and professional attitude toward work; The ability to work independently; and Possess prior experience in compiling a language database or building a language model, which will be considered an advantage. Terms and Conditions for Appointment The successful candidate will be appointed on a one-year temporary contract, renewable based on satisfactory performance. 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. 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.

Responsibilities

The appointee will assist the Principal Investigator on a research project focused on a linguistic method for detecting motorist intoxication to improve fleet driving safety management. Key duties include assisting with data collection and analysis, compiling a digitalized database, supporting language model building, and implementing advanced machine learning models.