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
Summary
Lead knowledge, retrieval, reasoning, and agent orchestration for a Digital ATCO Assistant using LLMs, Agentic AI, multi-agent architectures, and RAG. Requires a PhD or Master’s with research experience, 2-4 years hands-on LLM/Agentic AI experience, RAG and knowledge graph experience, strong Python, PyTorch, LLM frameworks, APIs, and microservices.
Responsibilities
Design and evaluate Agentic AI and multi-agent architectures; develop tool/function calling and RAG pipelines; design benchmarks and experiments; expose capabilities via APIs/microservices; translate operational use cases with domain experts; contribute to reports, proposals, publications, demonstrations.
Qualifications
PhD in AI/CS/ML/NLP or related discipline, or Master’s with substantial applied research experience; 2-4 years hands-on LLM/Agentic AI experience; RAG, vector databases, knowledge graphs, grounded generation, hallucination mitigation; strong Python, PyTorch, LLM frameworks, APIs, microservices; LoRA/PEFT, Graph RAG, on-premise deployment desirable; communication and collaboration; publications and safety-critical experience preferred.
Education requirements
PhD in AI, Computer Science, Machine Learning, NLP, or related discipline; or a Master’s degree with substantial applied research experience.
Experience requirements
2-4 years hands-on experience with LLM, Agentic AI, multi-agent architectures, tool/function calling, and prompt/context engineering.