Research Fellow (Statistics and Data Science)

The successful candidate will work with Dr. Michael Choi and co-supervised by Dr. Wenqian Chen on stochastic algorithms with applications in bioinformatics and protein folding under a project on MAPLE: Mechanistic Accelerated Prediction of Protein Secondary Structure via LangEvin Monte Carlo, supported by a Ministry of Education Tier 1 grant under the Data for Science and Science for Data collaboration scheme.

The main responsibilities of the position include:
• Develop and analyze new stochastic algorithms with applications in bioinformatics and protein folding
• Conduct numerical and possibly physical experiments to support the theoretical findings
• Conduct literature review in bioinformatics and protein folding

• Qualifications / Discipline:
Ph.D. degree in Applied Mathematics, Statistics, Computer Science, Physics or Biochemical Engineering with research interests in stochastic algorithms, bioinformatics and related fields.

• Skills:
Research level knowledge in understanding state-of-the-art stochastic algorithms in bioinformatics and protein folding. Ability to develop new algorithms in Python.

• Experience:  
Prior research experience in stochastic algorithms and bioinformatics is preferred.

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.

Location: Kent Ridge Campus

Organization: Science

Department : Statistics and Data Science

Employee Referral Eligible: No

Job requisition ID : 18761