• Contract
• Manager
• Masters
• Singapore

Closing On 09 Jan 2020

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

• Develop, design and implement spatiotemporal algorithms to combine geospatial mobility and land use mix data to develop predictive insights into urban mobility demand
• Extra key features and implement machine-learning based techniques to support automated prediction of urban mobility features
• Assist the overall project team to integrate the developed software components into the unified Web-based analysis platform
• Assist the R&D team with demos at relevant conferences, events and/or trade shows, and in preparation of research manuscripts

Qualifications

• Masters’ degree in Computer Science, Computer Engineering, Information Technology or technical engineering disciplines, from a reputable institution of higher learning
• Minimum 6 months of experience on research projects related to spatiotemporal analytics of urban mobility data
• Proven track record in development of spatiotemporal prediction and anomaly detection algorithms (e.g., clustering-based approaches), based on geospatial mobility data
• Proficiency with machine learning and deep learning toolkits and technologies (e.g., Python, PyTorch) is a plus
• Research publications related to urban crowdsourcing and mobility is highly desired
• Self-motivated individual who can work independently and also collaboratively with a small team of colleagues in an academic research environment