Job Description

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Job Title: Research Fellow (Astrophysics/Data Science)
Posting Start Date: 08/08/2025
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The successful candidate will work with Assistant Professor Marc Hon to develop and apply modern machine learning methods for the analysis of large-scale astronomical datasets, emphasizing time-domain astronomy. The research direction can flexibly utilize archival data from major space-based and ground-based observatories, including NASA's Kepler, TESS, and JWST missions, ESA's Gaia mission, and large-scale spectroscopic surveys such as LAMOST. Scientific research directions include-but are not limited to- stellar variability, exoplanets, transient events, and new astrophysical phenomena.
The main responsibilities of the position include:
- Conducting original research using large-scale datasets from space-based missions and ground-based surveys.
- Developing and applying machine learning and statistical approaches specifically tailored to the analysis of complex astronomical data, with emphasis on time series and multimodal data integration.
- Engaging in active collaboration with team members and, where appropriate, contributing to mentoring students or junior researchers involved in related projects.
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 fields