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Research Fellow (CQM/LN/Data Science and Statistics)

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Date: 22-Oct-2021

Location: Outram Campus, SG

Company: National University of Singapore

Job Description

The Centre for Quantitative Medicine (CQM) in Duke-NUS Medical School aims to be the centre of excellence in quantitative sciences for improving lives through biomedical research by leading and supporting scientific research, collaboration and education in quantitative sciences.

 

The selected candidate will perform a variety of research activities which include planning, organizing, and conducting research studies under the supervision of the Principal Investigator:

  • Apply machine learning and deep learning techniques to analyse large-scale electronic health records and physiological signal data (ECG).
  • Conceptualise new ideas and develop plans for independent research which could have a considerable influence and impact in medical informatics and health data science.
  • Write high-quality technical/methodological research papers, present research outcomes and develop connections with local and international researchers for collaboration work.
  • Provide guidance and mentorship to junior researchers, graduate students with occasional educational/instructional activities.
  • Perform other academic duties as required by the Principal Investigator.

Job Requirements

  • PhD in computer science/engineering/statistics with good mathematical knowledge and excellent research experience in machine learning and deep learning.
  • Proven track record of publications (as the first author) in reputable technical journals and/or top-tier machine learning conferences.
  • Has good programming skills in Python and R.
  • Possess interests in applying machine learning to solve real-world medical problems.
  • Able to conduct independent research as well as support team research activities.
  • A strong team player who is able to prioritise, multi-task and work collaboratively in a diverse workforce.

 

We regret that only shortlisted candidates will be notified.