Research Fellow (Astrophysics/Data Science)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading academic institution in Asia seeks a postdoctoral researcher to develop machine learning methods for analyzing astronomical datasets. The successful candidate will engage in flexible research areas including stellar variability and exoplanets. Candidates should have a PhD in a related field and strong analytical skills. This position provides a vibrant research environment with access to high-performance computing facilities.

Benefits

Travel support for conferences

Monthly housing allowance

Qualifications

• PhD in a quantitative discipline is required.
• Strong experience in data science and statistical analysis.
• Demonstrated experience in independent research with large datasets.

Responsibilities

• Develop and apply machine learning methods to astronomical datasets.
• Engage in research directions based on mutual interests.
• Participate in interdisciplinary collaborations.

Skills

Analytical and computational skills

Proficiency with Python

Familiarity with large datasets

Good communication skills

Education

PhD in Physics, Data Science, Computer Science, Astronomy, or related discipline

Job description

Interested applicants are invited to apply directly at the NUS Career Portal.

Your application will be processed only if you apply via the NUS Career Portal.

We regret that only shortlisted candidates will be notified.

Job Description

Applications are invited for a postdoctoral research position in the Department of Physics at the National University of Singapore, in the research group of Assistant Professor Marc Hon. The successful candidate will develop and apply modern machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy.

Research directions will be flexible and shaped according to mutual interests. Potential areas include — but are not limited to — stellar variability, exoplanets, transient phenomena, and the discovery of new astrophysical events. Projects may make extensive use of archival data from major facilities, including NASA’s Kepler, TESS, and JWST missions; ESA’s Gaia mission; and large-scale spectroscopic surveys such as LAMOST. Experience working with large, complex datasets is expected, and familiarity with modern machine learning methods will be considered an asset.

NUS offers a vibrant research environment, with access to high-performance computing facilities and opportunities for interdisciplinary collaborations. The position additionally offers travel support for conferences and meetings and a monthly housing allowance.

Qualifications

• Qualifications/ Discipline: PhD in Physics, Data Science, Computer Science, Astronomy, Astrophysics, or a related quantitative discipline.

Skills:

• Strong analytical and computational skills in data science, statistical analysis, or machine learning.
• Proficiency with Python and data analysis frameworks.
• Familiarity with large, complex datasets and modern computational techniques.
• Good written and oral scientific communication skills.

Experience:

• Demonstrated experience conducting independent research involving the analysis of large datasets.
• Familiarity with modern machine learning methodologies is strongly desirable.
• A publication record demonstrating relevant skills in astronomy, astrophysics, physics, computer science, or related field.

Applicants should submit a cover letter outlining their research interests and relevant experience, a curriculum vitae including a full list of publications, and a brief (2-3 page) description of research interests, and the contact information of three referees who can provide letters of recommendation.