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Postdoc Fellow in Network Analysis for Technological Innovation

The Singapore University of Technology and Design, established in collaboration with Massachusetts Institute of Technology (MIT), focuses on design research and education through an innovative interdisciplinary curriculum and research infrastructure with MIT. Professor Thomas Magnanti, former Dean of Engineering at MIT, is the Founding President of SUTD.

We are currently looking for a postdoctoral research fellow and doctoral student in the interaction of network analysis, data science and technological innovation, at the SUTD-MIT International Design Centre at SUTD. This project aims to create technology networks based on patent data, and analyze how the structure of technology networks may impact the innovation prospects of innovation agents and the evolution of technologies. The results are expected to guide inventors, firms and nations to identify their directions and pathways for innovation.

The postdoc research fellow will collaborate with Prof. Jianxi Luo at SUTD, and the international research team – Dr. Jeffrey Alstott (MIT-SUTD Joint Postdoc Fellow) Dr. Giorgio Triulzi (SUTD Postdoc Fellow, in transition to a MIT Postdoc Fellow in 2016) Dr. Bowen Yan (SUTD-MIT International Design Centre Postdoc Fellow) Prof. Christopher Magee (Professor of Engineering Systems at MIT; Co-Director of SUTD-MIT International Design Centre)

Compensation is very competitive, depending on experiences. Interested candidates should send a letter of interest, a CV, publications, recommendation letters, or inquiries to Prof. Jianxi Luo (luo@sutd.edu.sg).

Relevant skills and knowledge sets are below. Strong candidates will have expertise in one or more of the areas, but it is not necessary to even have experience in all areas.

  • real-world data analysis
  • programming
  • network theory
  • complex systems theory
  • engineering design
  • statistical analysis, including such methods as used in econometrics
  • machine learning
  • natural language processing
  • predictive models
  • experimental methods to identify the mechanisms of real-world systems

Candidates who have a demonstrated ability to wrangle a messy, real-world dataset to produce predictive models are particularly encouraged.

LabSingapore University of Technology and Design

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