(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 a Research Scientist to advance AI modeling for healthcare decision support. You will adapt open and approved models to chronic disease tracking, risk stratification, and prevention planning.

Collaborate with clinical teams to prepare data, run fine-tuning experiments, and evaluate safety and usability against standards. The role emphasizes reproducibility, documentation, and impact on patient care.

Qualifications

• PhD Degree in computer science, AI, biomedical informatics, computational science, data science or a related quantitative field.

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.

Skills

Python

PyTorch

Transformers

Prompt engineering

RAG

Eval pipelines

Education

PhD in computer science / AI / biomedical informatics / related quantitative field

Tools

Hugging Face Transformers

vLLM

Job description

Job Description

The Research Scientist will contribute to advanced AI modeling workstreams for healthcare decision support. The role focuses on adapting and optimizing open and institutionally approved models to healthcare use cases, such as chronic disease tracking, multimorbidity scenarios, risk stratification, medication management, preventive care, and longitudinal health planning.

The scientist will prepare structured training and evaluation data from clinical vignettes, synthetic cases, guideline-grounded knowledge bases, and longitudinal records where available. The role will support model adaptation, alignment experiments, performance optimization, and rigorous evaluation against clinical standards, safety constraints, and system 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.