Research Fellow (Astrophysics/Data Science)

NATIONAL UNIVERSITY OF SINGAPORE; Pasir Panjang; On-site; MYR 150,000 - 200,000; Full time

Job summary

A prominent university is seeking a postdoctoral researcher to join its Physics Department, focusing on machine learning applications in astronomical datasets. This role involves analyzing large-scale datasets and contributing to various research projects in areas such as stellar variability and exoplanets. Candidates should hold a PhD in a relevant field and possess strong analytical skills, proficiency in Python, and a track record of independent research.

Benefits

Travel support for conferences

Monthly housing allowance

Qualifications

• PhD in a related quantitative discipline.
• Demonstrated experience in conducting independent research with large datasets.
• Familiarity with modern machine learning methodologies.

Responsibilities

• Develop and apply machine learning methods for astronomical datasets.
• Conduct independent research on stellar variability, exoplanets, and astrophysical events.
• Collaborate in a vibrant research environment with high-performance computing facilities.

Skills

Analytical skills

Computational skills

Python proficiency

Data analysis frameworks familiarity

Scientific communication skills

Education

PhD in Physics, Data Science, Computer Science, Astronomy, Astrophysics

Job description

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

Your application will be processed only if you apply via 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.