• Contract
• Middle Management
• PhD
• Singapore | Closing On 01 Jan 2022

1-year contract

About Us

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together – building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities – all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

Job Description

• The research scientist (RS) will work with PI/Co-PIs to make progress on the project of “Learning by Doing in the Age of Big Data”. In this project, the team aims to build a novel Big Data framework to measure the "Learning By Doing" (LBD) effects for workers in the transport gig economy in Singapore. Our ambition is to measure LBD effects at not just the productivity level, which is easily tainted by other factors, but also at the skill level. We plan to achieve this by mining drivers' microscopic movement traces and trip fulfillment (including both taxi and ride-hailing drivers), and quantify drivers' skills in anticipating demands and competition from other drivers.
• The RS will assist PI/Co-PIs in formulating the problem, proposing and implementing algorithms, building prototypes, and writing research papers.

Qualifications

• PhD in Computer Science, Economics, or related disciplines
• Previous research experience on related topics is highly preferred
• Research background in game-theoretic modeling, and quantitative behavioral economics
• Proficient in at least one programming language such as Java or Python
• Knowledge and experience with the handling of large-scale spatial-temporal datasets are a big plus 
• Knowledge and experience with modern machine learning algorithms are desirable