aimlroles

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

Ntu Open · verified Oct 22, 2026
NTU Main Campus Full-time Mid-level AI Research

At a glance

Mid-level AI Research role at Ntu. NTU Main Campus · full-time.

Pay not stated AI Research salary

RoleAI Research
SeniorityMid-level
LocationNTU Main Campus
EmploymentFull-time
PostedOct 15, 2026 · 7d ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build and assess agent designs that split work into tasks, plan steps, coordinate, and run workflows for the Digital ATCO Assistant
  • Equip agents to select and use tools, APIs, and knowledge services, while checking inputs and handling returned results or errors
  • Create retrieval agents and full RAG flows for AIPs, NOTAMs, procedures, and regulations using semantic search and knowledge representations
  • Set up benchmarks for task success, teamwork between agents, tool use, retrieval accuracy, factual grounding, and hallucination
  • Shape controller use cases into research tests, support prototype integration, and contribute to reports, proposals, and publications

What you bring

  • A doctorate in AI, computer science, machine learning, NLP, or a related field, or a master’s with substantial applied research
  • Two to four years building with LLMs, agentic AI, coordinated agent systems, tool use, and prompt or context design
  • Practical work with RAG, vector stores, semantic search, knowledge graphs or structured representations, and grounded generation
  • Strong Python ability plus experience using PyTorch, LLM frameworks, APIs, microservices, and retrieval and factuality evaluations

Who this fits

This mid-level research position suits you if you can develop and evaluate LLM agents and retrieval systems. You’ll collaborate with technical leads, domain experts, and controllers on a prototype at NTU’s main campus in Singapore.

From the employer

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