Research Assistant, Safety Analytics
The successful candidates will contribute to research activities related to evaluation and development of machine learning models for classifying construction contractors based on their safety risks. The analysis would help to proactively identify at-risk contractors so that interventions can be implemented to prevent accidents. The candidates shall work under the supervision of the Principal Investigator (PI) to conduct academic research and/ or administrative work. Specific job activities may include:
- Conduct literature review on topics related to the project;
- Design machine learning experiments and analyses related to clustering and classifying of contractors;
- Collect and clean data obtained from the collaborators;
- Conduct unsupervised and supervised learning analyses;
- Liaise with collaborators on meetings and data collections;
- Write research papers, reports and proposals;
- And any other tasks required by the principal investigator.
- A Bachelor's degree within the fields of computer science, engineering, statistics, mathematics, or related field and with at least 2 years’ relevant work experience, and good knowledge, skills and expertise in relevant field
- Strong analytics and machine learning background;
- Competent with Python machine learning packages such as Scikit learn, Numpy, and Pandas;
- Willing to learn, independent, leadership qualities, team player, professional, proactive, communicative, and responsible; and
- Strong English writing and communication skills.
Candidates with the following will be preferred (not compulsory):
- Prior experience with safety research;
- Publications in peer-reviewed journals;
- Work experience in project management, construction or related industry.
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : The Built Environment
Employee Referral Eligible: No
Job requisition ID : 20036