Research Associate, AI for Science - Pipelines & HPC

National University of Singapore; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

National University of Singapore invites candidates to design and implement the Instrumentation Gym's foundational layers that connect scientific data, ML representations, and compute environments used by researchers

Responsibilities include building data pipelines and modular Python interfaces, enabling scalable, reusable workflows across multi-node HPC with Docker/Apptainer and Slurm. Strong communication with scientists is essential.

Qualifications

• Degree in a Computational, Engineering, or Physical-Science discipline.
• A Master's or Bachelor's with equivalent research-engineering experience.

Responsibilities

• Data pipelines — ingestion, metadata schemas, and provenance tracking for electron microscopy, X-ray, and light microscopy datasets; format normalisation so one lab's deposit is usable by another.
• Model interfaces — modular Python APIs that let representation models, tokenizers, and downstream models be swapped and composed without rewriting the pipeline around them.
• Baseline tokenization and cartography pipelines, built collaboratively with ML researchers and domain scientists, as reference implementations others adapt for new domains.
• LLM- and RAG-assisted interfaces for dataset navigation, workflow discovery, and user interaction — retrieval over the Gym's own datasets, models, workflows, and deposited challenges.
• Forward-compatibility to HPC — components that scale from single-GPU teaching instances to multi-node; containerised, reproducible, scheduler-friendly deployment on NUS HPC.
• Onboarding and enablement — semi-regular sessions for new Gym users; helping instructors build workflows that generate teaching material from their own sources plus Gym content.

Skills

Python

ML frameworks

Data pipeline

GPU compute

Software maintenance

Non-engineer communication

Scientific imaging data

HPC/Slurm

Education

Master's or Bachelor's with relevant research-engineering experience

Tools

Docker/Apptainer

Slurm

HDF5/TIFF/OME-TIFF/Zarr

Job description

National University of Singapore invites candidates to design and implement the Instrumentation Gym's foundational layers that connect scientific data, ML representations, and compute environments used by researchers

Responsibilities include building data pipelines and modular Python interfaces, enabling scalable, reusable workflows across multi-node HPC with Docker/Apptainer and Slurm. Strong communication with scientists is essential.