(Senior) Scientist, Computing & Intelligence, IAIC

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

A*STAR RESEARCH ENTITIES in Singapore seeks an AI Research Scientist to adapt foundation models for primary-care decision support, including hypertension and multimorbidity scenarios. The role focuses on enabling Singapore-specific clinical applications using state-of-the-art AI methods.

You will prepare structured training data from clinical vignettes and synthetic cases, and support supervised and reinforcement learning experiments with strict safety and guideline compliance.

Qualifications

• PhD in computer science, AI, biomedical informatics, computational science, data science or related field.
• Hands-on experience with Python and modern deep-learning frameworks (PyTorch, Hugging Face).
• Experience with LLM fine-tuning, prompt engineering, retrieval-augmented generation, evaluation pipelines.

Responsibilities

• Prepare model-ready datasets from clinical vignettes, synthetic cases, longitudinal records.
• Develop and run fine-tuning, instruction-tuning, and retrieval-augmented experiments for GP-oriented tasks.
• Implement evaluation scripts for guideline adherence, safety, and output quality.
• Support multimodal and longitudinal data modeling with patient histories and labs.
• Collaborate with clinical partners to translate requirements into model tasks and acceptance criteria.
• Document experiments and reproducibility steps for internal review.

Skills

PhD Degree in related field

Python

PyTorch

Hugging Face Transformers

LLM fine-tuning

Education

PhD Degree in computer science or related field

Tools

PyTorch

Hugging Face Transformers

vLLM

Retrieval‑augmented generation

Job description

Job Description

The AI Research Scientist will contribute to clinical foundation model workstream for primary-care decision support. The role will focus on adapting open and/or institutionally approved foundation models to Singapore primary-care use cases, such as hypertension and cardiometabolic-renal multimorbidity scenarios involving diabetes, dyslipidaemia, CKD risk, medication burden, preventive care and follow-up planning.

The scientist will prepare structured training and evaluation data from clinical vignettes, synthetic cases, guideline-grounded Q&A, and eHINTS-like longitudinal data extracts where available. The role will support supervised fine-tuning, reinforcement learning or preference alignment experiments, and model evaluation against clinical templates, guideline compliance, safety constraints and usability requirements.

Key Responsibilities

• Prepare model-ready datasets from clinical vignettes, synthetic consultation cases, longitudinal records and guideline-derived tasks.
• Develop and run fine-tuning, instruction-tuning, preference-alignment and/or retrieval-augmented generation experiments for GP-oriented clinical tasks.
• Implement evaluation scripts for guideline adherence, factuality, safety alerts, medication recommendations, missing-data handling and structured output quality.
• Support multimodal and longitudinal data modelling where patient histories, lab trends, medications, notes and care plans need to be represented over time.
• Work with clinical collaborators to translate hypertension and multimorbidity requirements into model tasks, prompts, labels and acceptance criteria.
• Document experiments, datasets, model cards, limitations and reproducibility steps for internal review.

Required Qualifications and Skills

• PhD Degree in computer science, AI, biomedical informatics, computational science, data science or a related quantitative field.
• Hands‑on experience with Python and modern deep‑learning frameworks such as PyTorch, Hugging Face Transformers, vLLM or equivalent.
• Working knowledge of LLM fine‑tuning, prompt engineering, retrieval-augmented generation, evaluation pipelines and reproducible ML experimentation.
• Ability to process structured and unstructured clinical data safely, including tabular records, notes, medications, lab values and guideline documents.
• Good understanding of model evaluation, error analysis, data quality control and responsible AI practices in healthcare or other high-stakes domains.

Preferred Experience

• Experience with clinical NLP, medical LLMs, healthcare datasets, FHIR/EMR-style data, or guideline-based decision support.
• Experience building synthetic data, simulation-based cases, or benchmark datasets for healthcare AI.
• Familiarity with primary-care chronic disease management, especially hypertension, diabetes, lipids, CKD risk and medication safety.
• Experience with GPU-based model training, experiment tracking, and deployment-oriented inference optimization.