Research Fellow, School of Computing
Research Fellow, School of Computing
National University Of SingaporeFresher
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Job Description
Job Title: Research Fellow, School of Computing
University-Level Unit: School of Computing
Faculty/Department-Level Unit: Department of Computer Science
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 23/09/2026
Job Description
The National University of Singapore invites applications for the position of Research Fellow 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.
The Research Fellow will develop empirical methods and AI systems for evaluating whether agentic AI genuinely augments human work. The role contributes to the CIVIC AI research agenda on workflow-level human–agent collaboration: measuring durable value beyond headline productivity, supporting meaningful human control, and examining how AI deployment affects accountability, learning, career pathways, and job purpose over time.
The Research Fellow will lead research that connects rigorous NLP and generative-AI methods with real organisational workflows. They will design benchmarks, workflow records, and evaluation protocols that make hidden verification, exception-handling, recovery, and factuality costs measurable. They will also develop and evaluate AI assistants that are analytically capable, evidence-grounded, and designed to preserve substantive human judgement rather than reduce oversight to approval theatre.
This position is particularly suited to a researcher with deep expertise in analytical data-to-text generation, factuality evaluation, benchmark construction, hierarchical reasoning, and production-grade LLM systems. The ideal candidate can move comfortably between foundational NLP research and deployed human-centred AI applications.
Job Requirements
Location: Kent Ridge Campus
Organization: School of Computing
Department : Department of Computer Science
Employee Referral Eligible: No
Job requisition ID : 34496
Req ID: 34496
University-Level Unit: School of Computing
Faculty/Department-Level Unit: Department of Computer Science
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 23/09/2026
Job Description
The National University of Singapore invites applications for the position of Research Fellow 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.
The Research Fellow will develop empirical methods and AI systems for evaluating whether agentic AI genuinely augments human work. The role contributes to the CIVIC AI research agenda on workflow-level human–agent collaboration: measuring durable value beyond headline productivity, supporting meaningful human control, and examining how AI deployment affects accountability, learning, career pathways, and job purpose over time.
The Research Fellow will lead research that connects rigorous NLP and generative-AI methods with real organisational workflows. They will design benchmarks, workflow records, and evaluation protocols that make hidden verification, exception-handling, recovery, and factuality costs measurable. They will also develop and evaluate AI assistants that are analytically capable, evidence-grounded, and designed to preserve substantive human judgement rather than reduce oversight to approval theatre.
This position is particularly suited to a researcher with deep expertise in analytical data-to-text generation, factuality evaluation, benchmark construction, hierarchical reasoning, and production-grade LLM systems. The ideal candidate can move comfortably between foundational NLP research and deployed human-centred AI applications.
Job Requirements
- PhD in Computer Science, NLP, AI, Computational Social Science, or related field
- Strong publication record in NLP, generative AI, data-to-text generation, evaluation, factuality, retrieval, or related areas.
- Demonstrated experience constructing high-quality real-world datasets and benchmarks rather than relying solely on synthetic tasks.
- Expertise in evaluating generative systems for factual grounding, reasoning quality, and practical usefulness.
- Strong Python and modern LLM-development experience, including PyTorch, Hugging Face, SQL, data-analysis tooling, and reproducible experimentation.
- Ability to build and evaluate end-to-end LLM applications, including retrieval, structured-data reasoning, tool use, and validation pipelines.
- Excellent written communication skills and the ability to collaborate across technical, social-scientific, governance, and practitioner communities.
Location: Kent Ridge Campus
Organization: School of Computing
Department : Department of Computer Science
Employee Referral Eligible: No
Job requisition ID : 34496
Req ID: 34496
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Key Skills
About Company
Skills:
Pytorch, Computer Vision, Python, software engineering best practices, clinical AI validation methodologies, deep learning frameworks, multi-modal medical imaging, ophthalmic disease understanding, Clinical Trial Design, medical device regulation, retinal imaging analysis, data harmonisation, regulatory considerations
Skills:
C, Python, agentic AI, system performance analysis, AI workflows, parallel computing algorithms
Skills:
causal inference , Time Series Analysis, Neural Networks, Machine Learning, Artificial Intelligence, Python, reinforcement learning, statistical computing, Statistics, Clinical trial design methodology, R, Bayesian Statistics, Diffusion models, Digital twins
Skills:
Retrieval Augmented Generation (RAG), Technical Documentation, Grounded Generation with Traceable Provenance, Structured Retrieval, Reasoning Methods, Vision-Language Models, Reproducible Evaluation Pipelines, Graph and Ontological Modelling, Evaluation Methodology, Multimodal Embeddings, Similarity-based Search, Multimodal Knowledge Representation, Large Language Models