Research Fellow (AI for Science) in Singapore

Q: What does a Research Fellow (AI for Science) do at NUS?

As a Research Fellow (AI for Science) at the National University of Singapore , you work as a Discovery Gym Lead embedded in the Physics department. You run weekly peer-learning cohorts for postgraduates and postdocs, lead Discovery Sprints with domain researchers, and turn real-world instrument data into working AI/ML models. You also deliver an interactive dashboard within one week and create pedagogical datasets for the Gym gallery. Learn more in our Research Fellow guide .

Q: What qualifications do I need to apply?

You need a Ph.D. in a Science or Engineering discipline , such as Physics , Chemistry, Biology, Materials Science, Chemical/Biomedical Engineering, Pharmacy, or food science. A publication track record and postdoctoral or industry experience applying machine learning in research are expected. For help shaping your application, use our free academic CV template and read these academic CV tips .

Q: Which technical skills are most important?

The role requires fluent scientific Python with a modern ML stack : PyTorch or JAX , scikit-learn , pandas , and NumPy . You should be able to build end-to-end machine learning pipelines , handle messy real-world instrument data , and ship a useful interactive dashboard on unfamiliar data within a week. Desirable skills include HPC or multi-GPU environments, containerised workflows , foundation models , and LLM/agentic workflows .

Q: Is this a postdoctoral role or a permanent staff position?

The position is categorised as Research Staff and is titled Research Fellow , so it sits between a traditional postdoctoral fellowship and a longer research staff appointment. It is suitable for candidates with postdoctoral or industry research experience. Compare it with our postdoctoral research fellow overview and browse more postdoctoral jobs .

Q: What are the working expectations and sprint structure?

You typically manage 1–2 active Discovery Sprints at a time plus 1–2 in support. Consultations should not exceed 40% of your week , active projects should wrap within about three months , and you are expected to produce at least one paper or proceeding per year . You also join the monthly Discovery Gym Lead methods-exchange and weekly syncs with the AI for Science Gym Architect .

Q: What is the application deadline and where is the job located?

Applications close on 02 November 2026 . The role is based at the Kent Ridge Campus of the National University of Singapore . Prepare a focused application using our free cover letter template and review common Research Fellow interview questions before applying.

University-Level Unit: Science

Faculty/Department-Level Unit: Physics

Employee Category: Research Staff

Location: Kent Ridge Campus

Posted On: 17/08/2026

Job Description

About the role

The AI for Science Gym builds bottom-up AI capability across NUS science and engineering. Its Discovery Gym helps researchers who already know the basics of data science and ML use AI to push the frontiers of their own fields.

As a Discovery Gym Lead you are an AI-enabled data scientist embedded in a scientific domain. You run the Gym for one or more departments — guiding a weekly small-group cohort of postgraduates and postdocs, and partnering with research groups to turn their instrument data into working AI/ML capability, then into teaching material the whole University can learn from.

Job description

Weekly small-group peer-learning in your department(s): participants work through hard problems on their own data and models, apprenticeship-style. Onboard each new department as it joins.
One-month Discovery Sprints with domain partners: assess dataset suitability, agree goals and provenance, deliver a working dashboard within week 1, then define research goals and go/no-go gates. Close each sprint by handing over skills and scripts, and extracting a pedagogical dataset for the Gym gallery.
Feed the shared substrate: deposit each partner's data as a tokenised pedagogical challenge (useful provenance, key annotations withheld), and contribute domain-workflow demos that bridge domain scientists and AI scientists.
Publish and compete: run sprints that yield a pedagogical preprint; take part in scrimmages against competing AI meta-harnesses.
Join the monthly DGL methods-exchange and the weekly sync with the Architect and other gym leads.

Working expectations:

1–2 active sprints at a time plus 1–2 in support; consultation ≤40% of your week; active projects wrapped within ~3 months; at least one paper or proceeding per year.

Qualifications

Ph.D. in a Science or Engineering discipline (e.g. Physics, Chemistry, Biology, Materials Science, Chemical/Biomedical Engineering, Pharmacy, food science).

Skills:

Strong data science and ML experience: end-to-end pipelines — wrangling, dimensionality reduction, clustering, labelling, supervised and unsupervised learning — and an understanding of where they break.
Fluent scientific Python with a modern ML stack (PyTorch or JAX, scikit-learn, pandas, numpy) and reproducible, version-controlled workflows.
Experience with messy real-world instrument data, and the judgement to tell a promising dataset from a hopeless one.
Ability to ship fast and visibly — a useful interactive dashboard on unfamiliar data within a week.
Excellent communication and teaching instincts; genuine fluency with AI-assisted development and the ability to teach it critically.
A publication track record and the appetite to keep publishing.

Experience:

Postdoctoral or industry experience applying ML in research.
HPC or multi-GPU environments; containerised workflows.
Representation learning, foundation/self-supervised models, or physics-informed methods.
Building or evaluating agentic/LLM workflows.
Mentoring, teaching, or running workshops and competitions.
Breadth across more than one scientific domain.

Req ID: 34083

Job details

Title: Research Fellow (AI for Science)
Employer: National University of Singapore
Location: Kent Ridge Campus
Published: Sep 3, 2026
Closes:: Nov 2, 2026
Job type: Full time, Academic / Faculty, Physics
Field: Computational Physics

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Frequently Asked Questions

🧪What does a Research Fellow (AI for Science) do at NUS?

As a Research Fellow (AI for Science) at the National University of Singapore, you work as a Discovery Gym Lead embedded in the Physics department. You run weekly peer-learning cohorts for postgraduates and postdocs, lead Discovery Sprints with domain researchers, and turn real-world instrument data into working AI/ML models. You also deliver an interactive dashboard within one week and create pedagogical datasets for the Gym gallery. Learn more in our Research Fellow guide.

🎓What qualifications do I need to apply?

You need a Ph.D. in a Science or Engineering discipline, such as Physics, Chemistry, Biology, Materials Science, Chemical/Biomedical Engineering, Pharmacy, or food science. A publication track record and postdoctoral or industry experience applying machine learning in research are expected. For help shaping your application, use our free academic CV template and read these academic CV tips.

💻Which technical skills are most important?

The role requires fluent scientific Python with a modern ML stack: PyTorch or JAX, scikit-learn, pandas, and NumPy. You should be able to build end-to-end machine learning pipelines, handle messy real-world instrument data, and ship a useful interactive dashboard on unfamiliar data within a week. Desirable skills include HPC or multi-GPU environments, containerised workflows, foundation models, and LLM/agentic workflows.

📋Is this a postdoctoral role or a permanent staff position?

The position is categorised as Research Staff and is titled Research Fellow, so it sits between a traditional postdoctoral fellowship and a longer research staff appointment. It is suitable for candidates with postdoctoral or industry research experience. Compare it with our postdoctoral research fellow overview and browse more postdoctoral jobs.

🔬What are the working expectations and sprint structure?

You typically manage 1–2 active Discovery Sprints at a time plus 1–2 in support. Consultations should not exceed 40% of your week, active projects should wrap within about three months, and you are expected to produce at least one paper or proceeding per year. You also join the monthly Discovery Gym Lead methods-exchange and weekly syncs with the AI for Science Gym Architect.

📝What is the application deadline and where is the job located?

Applications close on 02 November 2026. The role is based at the Kent Ridge Campus of the National University of Singapore. Prepare a focused application using our free cover letter template and review common Research Fellow interview questions before applying.