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Job Description

The National University of Singapore invites applications for the position of Research Associate in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large-scale experimental systems and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.

We invite applications for a Research Associate position in the area of efficient and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data analytics, and high-performance model design, aimed at developing next-generation intelligent systems that are both scalable and explainable.

This role bridges algorithmic research and systems implementation, offering opportunities to collaborate with leading academics and engineers on developing resource-efficient, adaptive learning frameworks for complex data environments.

Key Responsibilities:

• Design and implement ensemble learning algorithms and optimization strategies for large-scale or streaming data.
• Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency.
• Build and maintain robust data pipelines for high-throughput modeling over heterogeneous or sparse datasets.
• Conduct system profiling, model benchmarking, and empirical evaluation across different computing architectures.
• Explore novel strategies for interpretable ensemble modeling and adaptive decision systems.
• Contribute to research publications, technical reports, and open-source toolkits.
• Collaborate with faculty, postdoctoral researchers, and students on advanced machine learning research and prototype deployment.

Job Requirements

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or related discipline
• Strong programming proficiency in Python and C/C++.
• Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks).
• Hands-on experience with parallel or GPU-based computing (CUDA, OpenCL, or equivalent).
• Familiarity with data streaming, online learning, or real-time analytics frameworks.
• Solid understanding of machine learning algorithms, data structures, and numerical optimization.
• Experience with sparse data modeling or heterogeneous feature handling is advantageous.
• Proficiency with Linux, version control (Git), and performance debugging tools.
• Excellent analytical, communication, and problem-solving skills.