Job Summary

About the Role

The Institute of High Performance Computing (IHPC), A*STAR, is seeking a highly motivated Research Scientist to conduct cutting-edge research in Healthcare AI Safety, Trustworthy AI, and Responsible AI Systems.

The successful candidate will lead the development of methodologies, frameworks, and technologies that ensure AI systems are safe, reliable, robust, transparent, and clinically trustworthy for deployment in healthcare settings. The role will focus on advancing AI safety research while enabling the responsible translation of AI innovations into clinical practice and healthcare operations.

Key Responsibilities

Research & Innovation

• Conduct original research in Healthcare AI Safety, Trustworthy AI, Machine Learning, and Artificial Intelligence.
• Develop novel methodologies and frameworks in areas such as:
• AI Safety and Alignment
• Trustworthy and Responsible AI
• Explainable and Interpretable AI
• AI Evaluation and Benchmarking
• Robustness and Reliability
• Human-AI Collaboration
• AI Governance and Risk Management
• Design techniques for detecting, mitigating, and monitoring AI failures, biases, hallucinations, and unsafe behaviors.
• Develop methods for uncertainty quantification, confidence estimation, and risk-aware decision-making.

• Develop safety frameworks for foundation models, large language models, multimodal AI systems, and agentic AI systems used in healthcare.
• Design methodologies to evaluate clinical safety, reliability, fairness, and generalizability of AI systems across diverse patient populations and healthcare settings.
• Develop monitoring systems for post-deployment surveillance, model drift detection, and continuous safety assessment.
• Build mechanisms for human oversight, escalation, and safe human-AI collaboration in clinical workflows.
• Develop evaluation frameworks for clinical decision support systems, healthcare copilots, and autonomous healthcare agents.
• Advance approaches for safeguarding patient privacy, data security, and responsible use of healthcare data.

Translational AI Safety

• Collaborate with clinicians, healthcare providers, regulators, policymakers, and industry partners to support safe deployment of AI systems.
• Contribute to healthcare AI governance frameworks, safety standards, regulatory science, and best practices.
• Translate AI safety research into practical tools, platforms, and operational frameworks for healthcare organizations.
• Support technology transfer, intellectual property generation, and commercialization opportunities.

Scientific Leadership

• Publish high-impact research in leading AI, machine learning, healthcare, and AI safety conferences and journals.
• Contribute to grant proposals, project planning, and strategic research initiatives.
• Mentor junior researchers, engineers, and students.
• Represent IHPC in collaborations with healthcare institutions, government agencies, regulators, and industry partners.

Requirements

Essential

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Biomedical Informatics, Healthcare Informatics, or a related discipline.
• Strong track record in AI/ML research demonstrated through publications, patents, or impactful projects.
• Strong programming skills in Python and modern machine learning frameworks such as PyTorch or TensorFlow.
• Experience developing and evaluating machine learning and deep learning models.
• Strong interest in AI safety, responsible AI, healthcare applications, and trustworthy AI systems.

Preferred

• Experience in one or more of the following areas:
• AI Safety and Alignment
• Trustworthy and Responsible AI
• Explainable AI (XAI)
• Foundation Models and Large Language Models
• Agentic AI Systems
• Healthcare AI and Clinical Decision Support
• AI Evaluation and Benchmarking
• Human Factors and Human-AI Interaction
• Regulatory Science and AI Governance
• Healthcare Data Privacy and Security

What We Offer

• Opportunity to shape the future of safe, trustworthy, and responsible AI in healthcare.
• Access to world-class computational infrastructure, healthcare datasets, and research facilities.
• Collaboration with leading AI researchers, clinicians, healthcare providers, regulators, and industry partners.
• A dynamic environment supporting both fundamental AI safety research and real-world deployment.
• Opportunities to influence national and international healthcare AI safety standards and frameworks