Research Assistant – Sparse Boosting & Spatial Data Analytics

About the role

The School of Physical and Mathematical Sciences at NTU Singapore is seeking a Research Assistant to contribute to an AcRF Tier 1 project focused on sparse boosting for high‑dimensional spatial autoregressive models. You will work on methodological development, simulations, computational implementation, and real‑data applications, producing reproducible research outputs.

Key responsibilities

• Develop and implement sparse boosting algorithms for high‑dimensional spatial models.
• Design and run simulation studies, benchmark performance, and perform robustness checks.
• Optimize computational procedures and ensure efficient code execution.
• Apply methods to real‑world datasets and generate code, reports, presentations, and manuscript drafts.
• Collaborate with the principal investigator and research partners to meet project milestones and reporting requirements.

Required profile

• Minimum Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Econometrics, or a related discipline.
• Strong background in statistical modelling, high‑dimensional data analysis, and machine learning.
• Proficiency in R; experience with Python or similar languages is advantageous.
• Good analytical, programming, communication, and scientific‑writing abilities.
• Ability to work independently and as part of a team, adhering to project timelines.

Required skills

• R programming
• Python (optional)