At the Institute of High Performance Computing (IHPC), A*STAR, we drive innovation at the intersection of artificial intelligence and healthcare. Our mission is to build large language models (LLMs) and multimodal foundation models that can transform clinical decision support, biomedical discovery, and public health.

We are looking for AI talent at both Scientist and Engineer levels to join us. Whether you are a PhD-trained scientist pushing the frontiers of research, or an engineer with strong applied AI skills ready to build large-scale systems, you will contribute to innovations that make real impact in healthcare.

Key Responsibilities:

Research & Development

• Develop novel architectures, training strategies, and optimization methods for LLMs and multimodal foundation models.
• Advance instruction tuning, reinforcement learning (RLHF/GRPO), retrieval-augmented generation (RAG), and knowledge-grounded reasoning in medical AI.
• Design and implement multimodal learning pipelines that integrate text, imaging, omics, and real-world clinical data.

Translation & Collaboration

• Prototype AI systems for clinical and biomedical applications (e.g., decision support, conversational AI, population health, drug discovery).
• Benchmark models against state-of-the-art healthcare AI datasets and evaluation frameworks.
• Collaborate with industry and healthcare partners to enable translation, licensing, and commercialization.

Scientific Leadership

• Publish in top-tier AI/ML venues (e.g., NeurIPS, ICML, ICLR, AAAI, ACL) and high-impact biomedical journals (e.g., Nature Medicine).
• Contribute to open science initiatives (datasets, model releases, benchmarks).
• Secure funding through collaborative research proposals with academia, healthcare institutions, and industry.

Job Requirements:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
• Strong publication record in top AI/ML venues (e.g., NeurIPS, ICML, ICLR, ACL) or high-impact biomedical journals.
• Deep expertise in LLMs, foundation models, or multimodal AI.
• Strong skills in deep learning frameworks (PyTorch, TensorFlow, JAX) and distributed/HPC training.
• Experience in large-scale LLM and foundation model training and deployment.
• Experience with medical or biomedical data (EHR, clinical notes, imaging, genomics).
• Familiarity with biomedical ontologies and knowledge graphs (UMLS, SNOMED CT, PubMed).
• Understanding of AI ethics, safety, and regulatory considerations in healthcare.