Part-time Research Assistant I (R6920) (S&T) at Open University of Hong Kong

, Hong Kong, China -

Full Time

Start Date

Immediate

Expiry Date

16 Jun, 26

Salary

0.0

Posted On

18 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Programming, Data Preprocessing, Model Implementation, Training Pipeline Development, Parametric Modeling, Rhino, Grasshopper, CNNs, GNNs, Computational Methodologies, Model Validation, English, Chinese, Electrochemistry, Mathematical Model

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 Science and Technology: Major Duties and Responsibilities The appointee shall mainly assist the research team in a research project "AI Driven Daylight Performance Modelling and Optimization System for High-Density Urban Environment" (R6920). The appointee shall mainly be responsible for the following: Assisting in applying rigorous computational methodologies to establish and validate the core AI surrogate model; Conducting data preprocessing, model implementation, and training pipeline development; Conducting parametric modeling using Rhino/Grasshopper; Supporting comparative evaluation of neural network architectures, including CNNs (voxel grid processing) and GNNs (spatial relationship modeling of buildings); and Conducting other research activities as instructed by the Principal Investigator. Candidates Candidates should possess the following qualifications, experience, and competence: A master’s degree or above in Computer Science, Architecture, Engineering, or a related discipline; Strong background in machine learning and programming; Practical experience in model development and evaluation; Strong sense of responsibility, effective organizational and interpersonal skills; Very good command of English and Chinese; and Preference will be given to those with technical experience in electrochemistry or mathematical model. Terms and Conditions for Appointment Successful candidate will be appointed on a temporary part-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. 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 primarily assist the research team on the project "AI Driven Daylight Performance Modelling and Optimization System for High-Density Urban Environment." Responsibilities include applying computational methodologies to establish and validate the core AI surrogate model, conducting data preprocessing, and developing training pipelines.