Research Fellow (AI / Computer Science / Machine Learning / NLP)

Nanyang Technological University Singapore; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

NTU's Advanced Technology and Medical Research Institute (ATMRI) invites applications for a Research Fellow to lead the Digital ATCO Assistant project. You will drive knowledge, retrieval, reasoning, and agent orchestration using LLMs, Agentic AI, and multi-agent architectures.

You will implement tool calls and end-to-end RAG pipelines across aviation domains and operate within APIs/microservices. The role emphasizes collaboration with domain experts, controllers, and peers to shape research

Qualifications

• PhD or Master’s with substantial applied research in AI/ML/NLP.
• 2-4 years hands-on with LLMs and Agentic AI.
• Experience with RAG, vector databases, and knowledge graphs.
• Strong Python, PyTorch, and API/microservices skills.

Responsibilities

• Lead design and evaluation of Agentic AI and multi-agent architectures for the Digital ATCO Assistant.
• Develop tool calling to invoke tools, APIs, and knowledge services with validation and error handling.
• Build end-to-end RAG pipelines across AIPs, NOTAMs, and procedures using vector databases and knowledge graphs.
• Design benchmarks for task performance, coordination, and factual grounding.
• Collaborate with engineers and domain experts to expose capabilities via APIs and microservices.
• Translate operational use cases into research requirements, datasets, and test scenarios.
• Contribute to reports, proposals, publications, and collaborations with partners.

Skills

LLM

Agentic AI

Multi-agent architectures

Tool calling

Prompt engineering

Education

PhD in AI/CS/ML/NLP

Master’s with applied research

Tools

Python

PyTorch

LLM frameworks

APIs

Microservices

Vector databases

RAG

Job description

ATMRI at NTU is seeking a Research Fellow to join the Digital ATCO Assistant project. The role focuses on leading the assistant’s knowledge, retrieval, reasoning, and agent orchestration capabilities through LLMs, Agentic AI, multi-agent architectures, and Retrieval-Augmented Generation.

Responsibilities

• Design, develop, and evaluate Agentic AI and multi-agent architectures for task decomposition, planning, reasoning, coordination, and workflow orchestration within the Digital ATCO Assistant project.
• Develop tool/function calling capabilities that enable agents to select and invoke tools, APIs, and knowledge services, including input validation, interpretation of tool outputs, and error handling.
• Develop the retrieval agent and end-to-end RAG pipelines across AIPs, NOTAMs, operational procedures, and regulations using vector databases, semantic retrieval, and knowledge graphs or structured knowledge representations.
• Design benchmarks and conduct experiments to evaluate agent task performance, multi-agent coordination, tool-call correctness, retrieval precision/recall, factuality, grounding, and hallucination.
• Work with the integration engineer/technical lead to expose agent, retrieval, and reasoning capabilities through APIs and microservices for integration into the prototype.
• Work with domain experts and controllers to translate operational use cases into research requirements, evaluation datasets, and test scenarios.
• Contribute to technical reports, project proposals, publications, demonstrations, and collaboration with academic, industry, and regulatory partners.

Requirements

• PhD in AI, Computer Science, Machine Learning, NLP, or a related discipline; or a Master’s degree with substantial applied research experience.
• 2-4 years hands-on experience with LLM, Agentic AI, multi-agent architectures, tool/function calling, and prompt/context engineering.
• Experience with RAG, vector databases, semantic retrieval, knowledge graphs or structured knowledge representation, grounded generation, and hallucination mitigation.
• Strong Python skills and experience with PyTorch, modern LLM frameworks, APIs, microservices, and evaluation of retrieval precision/recall and factuality.
• Experience with LoRA/PEFT, in-context learning, Graph RAG, temporal RAG, hybrid symbolic-neural reasoning, on-premise deployment, or inference optimisation is highly desirable.
• Good communication and multidisciplinary collaboration skills; relevant peer-reviewed publications and experience in safety-critical domains are preferred.

We regret to inform that only shortlisted candidates will be notified.

Hiring Institution: NTU