Kerja Sepenuh Masa, Programme Lead, Precision Health Initiative@NUS (2-year contract) di National University Of Singapore Central Region (Singapore) - Maukerja

Job Description

PHI@NUS is a cross-institutional initiative spanning the Yong Loo Lin School of Medicine, Duke-NUS Medical School and NUHS, established to advance Singapore’s national precision health agenda under RIE 2030. It brings together population-scale genomic and phenotypic data with functional biological validation to develop Asian-specific in silico models of health and disease.

The role will work closely with the Executive Director to translate PHI@NUS’s strategic vision into an executable portfolio of programmes, providing scientific and programme leadership across computational and data science, cross-institutional integration, data access, talent, partnerships and governance.

Scientific Leadership: Computational and Data Science

• Establish and lead a centralised computational and data-science platform using a federated, fractional-support model to support clinician- and biologist-led projects across three institutions.
• Provide scientific direction in bioinformatics, computational biology, population and statistical genomics, and facilitate application of computational approaches across clinical and basic science research.
• Drive AI adoption in bioinformatics workflows, including emerging agentic AI approaches, and support predictive and systems-genetics models for variant functionalisation, disease modelling and therapeutic target discovery.
• Establish standards for compute infrastructure, data governance and model validation to ensure reproducible and reusable models.
• Contribute to the development of PHI@NUS’s strategic framework.

Strategy and Programme Development

• Translate strategic priorities into seed and pilot programmes with clear milestones, deliverables and exit criteria.
• Support internal fund allocation and reallocation, and lead/co-develop major competitive grant proposals.
• Represent PHI@NUS in relevant internal and external scientific forums and engagements.

Cross-Institutional Integration and Data Analysis

• Lead strategies for integrating genomic and phenotypic data across institutions and relevant national platforms, including TRUST, SIMFONI, NSCC, PRECISE and MOH data-governance bodies.
• Build strategic relationships across the institutional, national and research ecosystem.
• Establish data-sharing, IP and collaboration frameworks with A*STAR and other ecosystem partners.

Talent, Teams and Capability Building

• Recruit, mentor and retain senior staff scientists in computational sciences, functional genomics and screening.
• Develop appropriate scientific career pathways aligned with RIE 2030 talent objectives.
• Foster collaborative relationships with computational professionals and professors, including those with partial PHI@NUS appointments.
• Build multidisciplinary teams, coach junior clinical and basic science faculty in grantsmanship and project formulation, and develop future scientific leaders.

Operations and Governance

• Work with the Executive Director to establish fund-tracking and project-management systems and provide oversight of senior project managers.
• Ensure compliance with NUS, NUHS and national requirements for research governance, ethics, biosafety and data protection.
• Contribute to the planning of the computational and screening cores, including their physical and organisational structure, co-location and information flow.

Qualifications

• PhD and/or MD in a relevant discipline, such as computational biology, genetics/genomics, bioinformatics or biomedical data science.
• At least 10 years of post-PhD experience, or equivalent, encompassing research leadership and either research management or translation. Experience across both academia and industry is preferred.
• Deep expertise in computational biology, genomics and population/statistical genetics, with a track record of applying large-scale data across multiple disease areas.
• Working familiarity with AI/ML, including emerging agentic AI approaches in the life sciences.
• Strong track record in building and leading scientific teams and platforms, and in developing effective multi-institutional collaborations with tact and diplomacy.
• Good understanding of Singapore's research, innovation, clinical and data-governance ecosystem, including national precision medicine programmes, healthcare clusters, A*STAR and national data platforms.
• Established scientific credibility, demonstrated through a substantive publication and/or patent record, sufficient to command the confidence of senior PIs and clinicians.