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

Nanyang Technological University, Singapore (NTU Singapore); Singapore; On-site; SGD 70,000 - 120,000; Full time

Job summary

ATMRI at NTU Singapore seeks a Research Fellow to lead the Digital ATCO Assistant project, focusing on knowledge, retrieval, reasoning, and agent orchestration with LLMs and multi-agent architectures. You will design, implement, and evaluate agentic AI systems and tool calling capabilities within a prototype environment.

The role requires PhD or substantial applied research in AI, with strong Python experience and familiarity with PyTorch, vector databases, and knowledge graphs.

Qualifications

• PhD or Master’s with substantial applied research in AI/CS/ML/NLP.
• Hands-on experience with LLMs, Agentic AI, and multi-agent architectures.
• Experience with retrieval augmented generation and knowledge representations.
• Strong Python skills and experience with modern LLM frameworks.

Responsibilities

• Design agentic AI architectures and multi-agent task decomposition.
• Develop tool calling capabilities for tool/APIs and handle outputs/errors.
• Build RAG pipelines across AIPs, NOTAMs, and procedures using vector DBs.
• Design benchmarks to evaluate performance, grounding, and hallucination.
• Expose capabilities via APIs/microservices for prototype integration.
• Translate use cases into research datasets and test scenarios.
• Contribute to reports, proposals, and publications with partners.

Skills

LLM

Agentic AI

Multi-agent systems

Tool calling

Prompt engineering

Context engineering

RAG

Vector databases

Semantic retrieval

Knowledge graphs

Python

PyTorch

LLM frameworks

APIs

Microservices

LoRA/PEFT

In-context learning

Graph RAG

Temporal RAG

Symbolic-neural reasoning

On-prem deployment

Inference optimization

Collaboration skills

Education

PhD in AI/CS/ML/NLP

Master's with applied research

Tools

Vector databases

Knowledge graphs

APIs

Microservices

PyTorch

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