Research Fellow (infectious Disease Modelling And Machine Learning For Public Health)

Singapore, Singapore

Job Description




Research Fellow in Infectious Disease Modelling and Machine Learning for Public Health

The National University of Singapore\'s Saw Swee School of Public Health (NUS) seeks a dedicated, skilled research fellow to join its multidisciplinary team. This position offers a unique opportunity to participate in innovative research projects on various topics such as infectious disease modelling, phylogeny, and characterizing human mobility patterns. The project allows you to collaborate with school personnel ( ), the Institute of Data Science ( ), and global leaders (mlgh.net/). More information about Swapnil Mishra can be found here:

Key Responsibilities:

  • Conduct high-quality research in infectious disease modelling, human mobility patterns, and/or machine learning applied to public health and global health.
  • Develop and implement computational models, statistical methods, and machine learning algorithms to analyze infectious disease data.
  • Collaborate with multidisciplinary teams of researchers, including epidemiologists, biologists, machine learners, biostatisticians, and public health experts.
  • Analyze large-scale datasets to generate insights into human mobility patterns using survey, mobile, or social media data.
  • Present research findings at national and international conferences and publish results in peer-reviewed scientific journals.
  • Assist in the supervision and mentoring of junior researchers.
  • Contribute to grant proposals and progress reports for funding agencies.
Interested applicants should submit the following documents during the application:
  • A cover letter explaining your interest in the position, relevant experience, and research interests.
  • A comprehensive curriculum vitae, including a list of publications (if applicable).
  • A brief research statement (maximum two pages) outlining your research experience and future aspirations.
  • Contact information for two professional references who can provide letters of recommendation upon request.
Review of applications will begin immediately and continue until the position is filled. The anticipated start date is February 2024, but this is negotiable. The initial appointment will be for one year, with the possibility of renewal based on performance and funding availability.

NUS is an equal-opportunity employer committed to diversity and inclusion. We welcome applications from all qualified individuals, regardless of race, color, religion, gender, sexual orientation, age, national origin, or disability.

In case of any questions or queries, please do not hesitate to contact Ass. Prof Swapnil Mishra at \'swapnil dot mishra at nus dot edu dot sg\' with the subject line \'Research Fellow in Infectious Disease Modelling, and Machine Learning for Public Health\'.

Qualifications
  • A PhD in a relevant field such as computer science, statistics, epidemiology, computational biology, bioinformatics, biostatistics, machine learning, or a related discipline.
  • Solid background in Bayesian inference, statistical modelling, and graph networks.
  • Proficiency in programming languages such as R or Python.
  • Prior experience in working with Stan or PyMC or NumPyro or Turing, or any other probabilistic programming language.
  • Excellent written and verbal communication skills, including presenting complex concepts to diverse audiences.
  • Demonstrated ability to work independently and as part of a multidisciplinary team.
  • A history of publications in high-quality peer-reviewed journals.
More Information

Location: Kent Ridge Campus
Organization: Saw Swee Hock School of Public Health
Department : Saw Swee Hock School of Public Health
Employee Referral Eligible: No
Job requisition ID : 22858

Contact list for further enquiries

Hiring Manager: [[Dr Swapnil Mishra]]
Hiring Manager Email: [[ ]]

Times Higher Education

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Job Detail

  • Job Id
    JD1389366
  • Industry
    Not mentioned
  • Total Positions
    1
  • Job Type:
    Full Time
  • Salary:
    Not mentioned
  • Employment Status
    Permanent
  • Job Location
    Singapore, Singapore
  • Education
    Not mentioned