Programme Lead, Precision Health Initiative@NUS (2-year contract)

National University of Singapore; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

The National University of Singapore seeks a senior scientific leader to drive computational and data-science initiatives across PHI@NUS. You will translate strategic vision into executable programmes, guiding research leaders, data governance, and cross-institutional collaborations to advance precision health.

Applicants should have a PhD/MD with a strong track record in computational biology, genomics and AI adoption in life sciences, plus 10+ years of post-PhD experience and leadership across

Qualifications

• PhD and/or MD in a relevant biomedical data science field.
• Extensive experience leading research groups and multi-institution projects.
• Strong publication and/or patent track record.

Responsibilities

• Lead centralized computational and data-science platform across three institutions.
• Provide scientific direction in bioinformatics, population/genomic statistics and AI adoption.
• Drive governance, data sharing frameworks and model validation standards.
• Translate strategic priorities into seed and pilot programmes with clear milestones.
• Build and mentor senior staff scientists and foster cross-institution collaborations.
• Oversee fund-tracking, project management and compliance with governance frameworks.

Education

PhD and/or MD in computational biology, genetics/genomics, bioinformatics or biomedical data science

10+ years post-PhD experience

Academia and industry experience

Publication and/or patent record

Job description

Company description

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

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.