Research Associate (Physics) in Singapore

Q: What qualifications do I need for this Research Associate (Physics) role?

You'll need a Degree in a Computational, Engineering, or Physical-Science discipline ; a Master's or Bachelor's with equivalent research-engineering experience is accepted. The role sits at the intersection of scientific data , ML representations , and HPC compute , so practical experience building data pipelines and reproducible ML workflows matters more than formal credentials alone. For guidance on presenting research-engineering experience, see how to write a winning academic CV . You can also explore other research jobs to compare qualification patterns.

Q: Which technical skills are most important for this position?

Strong Python and a modern ML framework ( PyTorch preferred) are essential. You should have experience building data pipelines and reproducible ML workflows end to end, comfort with GPU compute and scientific data formats such as HDF5, TIFF/OME-TIFF, and Zarr, and a track record of modular, maintainable software . Nice-to-have skills include LLMs and retrieval-augmented generation (RAG) , scientific imaging data , and HPC/cloud GPU deployment with Slurm, Docker/Apptainer. Explore more research roles or physics-informed neural networks for context on ML in physical sciences.

Q: What HPC and deployment experience is expected?

You should be comfortable deploying ML services on HPC or cloud GPU infrastructure , including Slurm , Docker/Apptainer , and distributed training . The role requires building components that scale from single-GPU teaching instances to multi-node HPC , containerised, reproducible, and scheduler-friendly for NUS HPC . If you have open-source contributions in scientific Python or ML tooling, that is a plus. For more on research infrastructure roles, visit research jobs or administration jobs .

Job Description

About the Role

The AI for Science Gym builds bottom-up AI capability across NUS science and engineering. Underneath its training gyms sits the Instrumentation Gym — the shared substrate of curated datasets, trained models, reusable workflows, and the harness that connects them on NUS HPC. You will design and build that substrate's foundational layers. These are the entry points through which most researchers engage with the Gym, and the patterns you set determine how cleanly the system scales onto multi-node HPC. The role sits at the interface between scientific data, ML representations, and the compute environments researchers use day-to-day.

Job Description

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.

Qualifications

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

Skills:

Strong Python and a modern ML framework (PyTorch preferred).
Experience building data pipelines and reproducible ML workflows end to end, not just training scripts.
Comfort with GPU compute and scientific data formats (HDF5, TIFF/OME-TIFF, Zarr, or instrument-native equivalents).
Track record of modular, maintainable software — tested, documented, and successfully picked up by people who didn't write it.
Interest in working at the intersection of science, ML, and systems engineering, and the temperament to stay there.
Ability to communicate with non-engineers — much of the job is understanding what a scientist actually needs.
Scientific imaging data — electron microscopy, X-ray/synchrotron, or light microscopy, including its artefacts and metadata conventions.
LLMs and retrieval-augmented generation: embeddings, vector stores, and the failure modes of retrieval over technical corpora.
Interactive data tools — notebook UIs, dashboards, viewer or annotation apps.
Deploying ML services on HPC or cloud GPU infrastructure: Slurm, Docker/Apptainer, distributed training.
Representation learning, self-supervised or foundation models, or tokenization for non-text modalities.
Open-source contributions in scientific Python or ML tooling; FAIR data practice.

Req ID: 34084

Job details

Title: Research Associate (Physics)
Employer: National University of Singapore
Location: Kent Ridge Campus
Published: Aug 26, 2026
Closes:: Oct 25, 2026
Job type: Full time, Staff / Administration
Field: Laboratory and Research

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Frequently Asked Questions

🎓What qualifications do I need for this Research Associate (Physics) role?

You'll need a Degree in a Computational, Engineering, or Physical-Science discipline; a Master's or Bachelor's with equivalent research-engineering experience is accepted. The role sits at the intersection of scientific data, ML representations, and HPC compute, so practical experience building data pipelines and reproducible ML workflows matters more than formal credentials alone. For guidance on presenting research-engineering experience, see how to write a winning academic CV. You can also explore other research jobs to compare qualification patterns.

🔬What does the AI for Science Gym at NUS do?

The AI for Science Gym builds bottom-up AI capability across NUS science and engineering. Its Instrumentation Gym provides curated datasets, trained models, reusable workflows, and a harness connecting them on NUS HPC. This role focuses on designing foundational layers — data pipelines, model interfaces, and baseline tokenization/cartography pipelines — that most researchers use to engage with the Gym. Read about related ML physics simulations advancing engineering to understand the broader research context.

💻Which technical skills are most important for this position?

Strong Python and a modern ML framework (PyTorch preferred) are essential. You should have experience building data pipelines and reproducible ML workflows end to end, comfort with GPU compute and scientific data formats such as HDF5, TIFF/OME-TIFF, and Zarr, and a track record of modular, maintainable software. Nice-to-have skills include LLMs and retrieval-augmented generation (RAG), scientific imaging data, and HPC/cloud GPU deployment with Slurm, Docker/Apptainer. Explore more research roles or physics-informed neural networks for context on ML in physical sciences.

🔍Is experience with scientific imaging data required?

It is not listed as a hard requirement, but it is strongly valued. Knowledge of electron microscopy, X-ray/synchrotron, or light microscopy — including artefacts and metadata conventions — will help you design provenance tracking and format normalisation for the Instrumentation Gym. If you have worked with HDF5, TIFF/OME-TIFF, or Zarr, that experience is directly relevant. See related particle physics research for domain science context, or browse research assistant jobs.

⚙️What HPC and deployment experience is expected?

You should be comfortable deploying ML services on HPC or cloud GPU infrastructure, including Slurm, Docker/Apptainer, and distributed training. The role requires building components that scale from single-GPU teaching instances to multi-node HPC, containerised, reproducible, and scheduler-friendly for NUS HPC. If you have open-source contributions in scientific Python or ML tooling, that is a plus. For more on research infrastructure roles, visit research jobs or administration jobs.