Singapore-MIT Alliance for Research and Technology (SMART) Centre, Postdoctoral Researcher, Mobility Data Analysis

The application deadline for this job posting has passed. Although you can still view the information no new applications for this job are currently being accepted.
Reference: JOB36619
Application deadline: CLOSED

Project Overview

The Future Urban Mobility Interdisciplinary Research Group (FM IRG) from Singapore-MIT Alliance for Research and Technology (SMART) Centre is looking for an outstanding and promising postdoctoral researcher in the area of mobility data analysis. This project deals with a multitude of ubiquitous data including GPS, accelerometer, public transit smartcards, toll road readers, car loop counters, telecom data, and many others.

This position will be based at the SMART Centre in Singapore, located at the CREATE campus, an international research complex that has representatives from many renowned research institutions in the world (e.g. MIT, ETHZ, Technion Israel, TU Munchen, and UC Berkeley).

Job Description

For this position, the researcher will work on developing models/algorithms for real time traffic prediction that particularly take advantage of contextual data (e.g. data obtained from web-mining, news feeds, weather information) that can be relevant in terms of mobility phenomena. A simple case is that of a special events, such as a sports game, or a music concert. While these may have significant impact to the transport system, the traditional sensors (e.g. car counters, GPS probes, etc.) are insufficient to perceive this impact ahead in time. On the other hand, plenty of these events are advertised long time in advance in the web.

This general goal involves a vast list of research challenges at the level of machine learning, information retrieval, information extraction and transport engineering. Specifically, the job scope is as follow:

  • Typical data analysis process (cleaning, preparation, descriptive statistics, exploration);
  • Pattern recognition modeling (advancing current state of the art);
  • Performance assessment (complexity analysis, field testing);
  • Advance research in any of these areas or related.


The ideal person will have strong background in pattern recognition and computer science. but with a multidisciplinary spirit, namely understanding or willing to learn about urban mobility, transportation and planning. S/he will necessarily have to face statistical and mathematical challenges that are hard and somewhat rare in computing courses, so a good background in these topics is sought.

Candidates should meet the following requirements:

  • PhD in Computer Science or any other field that guarantees background for above description (e.g. physics, electrical engineering, mathematics, transportation);
  • Experience in one or more of the following sub-topics: bayesian machine learning framework, graphical models, statistical learning.
  • Good communication skills

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