Senior Scientist, Healthcare & Medtech, Iaic

A Research Scientist position is available immediately at the Institute for Infocomm Research (I²R), A*STAR, Singapore. The position focuses on advancing AI methodologies for precision medicine, digital health, and clinical decision support applications. Specific areas of focus include: Multimodal machine learning and foundation models for healthcare, leveraging the span of structured electronic health records, clinical notes, lifestyle data, and/or genomic data. Self-supervised, representation learning, and multimodal pretraining approaches for healthcare data. Learning from large-scale observational, longitudinal, irregularly sampled, and noisy healthcare datasets. Multiscale temporal modelling of patient trajectories and disease progression. Uncertainty quantification, explainable AI, trustworthy machine learning. Applications in disease screening, precision prevention, risk stratification, phenotyping, outcome prediction, biomarker discovery, and population health management. The role offers a unique opportunity to work at the intersection of healthcare, artificial intelligence, and clinical translation. Successful candidates will develop novel AI algorithms and systems using real-world healthcare datasets while collaborating closely with experts in AI, public health, precision medicine as well as with clinicians, health ecosystem stakeholders, and government entities on ambitious projects that have the potential to transform patient care and deliver improved health outcomes. Candidates should possess strong expertise across healthcare data understanding, deep learning problem formulation, algorithm, architecture and model development, and end-to-end AI system implementation. Experience in translating research innovations into real-world healthcare impact is highly desirable. Qualification and Field of Study Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Biomedical/Health Informatics, Biomedical Engineering, Electrical Engineering, Applied Mathematics, Statistics or a related quantitative discipline. Strong computational and research background, experience working with large-scale real-world healthcare datasets, and demonstrated expertise in at least two or more of the following areas: Machine learning and deep learning for healthcare applications, including multimodal learning, predictive modelling, representation learning, and foundation models. Self-supervised learning, multimodal pretraining, transfer learning, data-efficient learning and parameter-efficient adaptation techniques. Clinical natural language processing (NLP), large language models (LLMs), retrieval-augmented generation (RAG), and healthcare AI agents. Learning from heterogeneous, noisy, sparse, irregularly sampled, or weakly supervised healthcare data. Explainable AI, uncertainty quantification, trustworthy AI, and responsible deployment of AI in healthcare settings. Development of AI solutions for real-world clinical and digital health applications, from problem formulation to deployment, evaluation and real-world impact assessment of AI technologies. A strong track record with publications in leading AI, applied machine learning/mathematics/ statistics and digital health venues is required. Experience working in large interdisciplinary project teams with a health or medical technology translation perspective is a plus. Min. Years of Experience 4 years post-PhD with track record in applied projects and/or publishing research in leading AI and digital health venues. Experience in healthcare, corporate or application-oriented environments with exposure to operational workflows and clinical implementation challenges desirable. Other Requirements (e.g. Skills, Competencies) Competencies Strong expertise in modern AI approaches including multimodal data integration, transformers, generative AI and foundation models, trustworthy AI. Hands-on experience in rapid prototyping, model development, experimentation, evaluation and clinical validation to translate research ideas into deployable AI solutions. Deep intuition for modeling with large-scale, complex, and heterogeneous healthcare datasets (incl. real-world EHR, clinical narratives, imaging, wearables data, and/or -omics data). Effective collaboration with multidisciplinary domain stakeholders – with ability to rapidly learn the domain and experience bridging clinical, AI, implementation and business disciplines. Strong scientific communication skills, including publicati…