Job Description

The successful candidate will work with Dr. Linda Tan on the project, \xe2\x80\x9cImproving the efficiency of computational algorithms for fitting multilevel models to large data using augmentation techniques\xe2\x80\x9d, which is supported by the Academic Research Council, Ministry of Education Tier 2 grant.

In this project, the candidate will conduct an in-depth study of augmentation techniques and build upon existing techniques to develop novel algorithms for fitting complex models to large data sets. Models considered in this project (e.g., time series models for financial data and hierarchical spatial models for areal data) have wide applications and are designed for data which are often available in large quantities. The development of more efficient algorithms will enable useful insights to be drawn swiftly from these data.

This position is renewable on a yearly basis, for up to three years subject to satisfactory performance.

The main responsibilities of the position include:

• Performing literature review.
• Developing novel augmentation schemes and computational algorithms.
• Coding computational algorithms and running numerical experiments.
• Publishing original research articles.
• Presenting the research output at international conferences.

Qualifications

Qualifications / Discipline:

• Ph.D. degree in Mathematics, Statistics or similar fields.

Skills:

• Programming in R, Matlab and/or Julia.
• Good writing skills for producing publications.
• Responsive and able to work well independently and within a team.

Experience:

Experience with Bayesian inference, vector differential calculus, EM, variational approximation and MCMC algorithms is preferred.

Covid-19 Message

At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.

Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.

More Information

Location: Kent Ridge Campus
Organization: Science
Department : Statistics and Data Science
Employee Referral Eligible: No
Job requisition ID : 19017

Times Higher Education