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Job Description
We are looking for a research engineer the National University of Singapore (NUS) to support a project in the broad area of AI and machine learning.
You will help define the "best achievable" limits of Symbolic Regression (SR), which is a powerful modern machine learning technique. You will lead the algorithmic pillar of this project, developing high-performance software to conduct large-scale searches and benchmark empirical bounds. This work is critical for explainable AI (XAI) and AI for Science, moving beyond "black box" models to discover interpretable mathematical laws.
Key Responsibilities:
• Scale: Build and manage pipelines for large-scale exhaustive model searches.
• Benchmark: Execute rigorous hyperparameter tuning and performance benchmarking.
• Deploy: Assist in applying SR-informed models to real-world healthcare and scientific datasets.
• Open Science: Curate and release large-scale research datasets to the global community.
Qualifications
Technical Skills
• Coding: Proficiency in Python (NumPy, Pandas, Scikit-learn). Knowledge of C++ or SymPy is a major plus.
• Machine Learning: Strong grasp of ML fundamentals (generalization, loss functions). Experience with SR libraries (e.g., PySR, gplearn) is highly preferred.
• Systems: Experience running experiments on HPC clusters or cloud environments.
• Documentation: Ability to use LaTeX for technical reporting.
Qualifications
• Education: Degree in CS, Electrical Engineering, Math, or Physics.
• Analytical: Comfortable with mathematical theory (bounds, stability) and data-driven discovery.
• Proactive: Able to bridge the gap between theoretical research and scalable software engineering.
Candidate must be open to fixed term contract.
