Kerja Sepenuh Masa, Assistant Professor, AI Pathogen Genomics di National University Of Singapore Central Region (Singapore) - Maukerja

Job Description

The Duke-NUS Centre for Outbreak Preparedness (COP) works to strengthen communicable disease preparedness and response capacity across South and Southeast Asia. COP also hosts the Asia Pathogen Genomics Initiative (Asia PGI), a regional network supporting 15 countries to strengthen pathogen genomics surveillance, data use and translation for public health action. The Duke-NUS Centre for Biomedical Data Science (CBDS) provides a strategic hub for data science, artificial intelligence, statistical methodology, computational omics and systems biology, supporting biomedical discovery and translational impact across Duke-NUS, SingHealth, the Academic Medical Centre and wider regional and global partners. This joint appointment will advance research and platform development at the interface of AI and pathogen genomics, bringing together COP’s applied focus on infectious disease threats and regional preparedness with CBDS’s depth in biomedical data science, AI and computational biology.

Key responsibilities will include:

• Lead and develop applied research at the interface of AI, pathogen genomics and outbreak preparedness, in close collaboration with COP and CBDS faculty and partners.
• Design, develop and evaluate AI-enabled methods for pathogen genomic analysis, including approaches for variant detection, lineage classification, antimicrobial resistance prediction, outbreak clustering, genomic representation learning, multi-modal data integration and decision support.
• Build collaborations across Duke-NUS, SingHealth, national public health agencies, regional academic partners, ministries of health, international organisations and technology partners.
• Contribute to grant proposals, research strategy, donor engagement and externally funded programmes in AI, pathogen genomics, infectious disease surveillance and regional health security.
• Publish high-quality scientific papers, technical reports, policy briefs and other outputs that advance the field and support translation into public health practice.
• Support robust, reproducible and ethically governed analytical workflows, including attention to data quality, privacy, security, sovereignty, transparency, validation and responsible AI.
• Provide scientific and technical leadership to research staff, data scientists, students and collaborators, including supervision, mentorship and capacity development.
• Contribute to education and training in AI, computational genomics, biomedical data science and outbreak analytics for Duke-NUS learners and regional partners.
• Help translate analytical outputs into practical insights for surveillance, preparedness, outbreak response and policy decision-making.
• Perform other related duties as required by COP and CBDS leadership.

Qualifications

Appointment level will be commensurate with qualifications and experience, including evidence of research independence, scientific output, leadership, grant development, teaching or mentorship, and ability to work across academic, public health and technical teams. Minimum qualifications include:

• PhD or equivalent doctoral qualification in artificial intelligence, machine learning, computational biology, bioinformatics, genomics, infectious disease modelling, biomedical data science, statistics, computer science or a closely related field.
• Minimum experience commensurate with appointment: Senior Research Fellow - normally at least 5 years of postdoctoral or equivalent research experience; Principal Research Scientist - normally at least 6 years of relevant experience with evidence of research independence and leadership; Assistant Professor (Practice Track) - substantial applied research/practice experience with evidence of teaching, mentorship or capacity development.
• Demonstrated expertise in AI/data science methods applied to genomics, omics, infectious diseases, public health surveillance or related biomedical domains.
• Strong technical skills in areas such as machine learning, statistical learning, scalable analytical pipelines, software engineering, cloud or high-performance computing, federated or privacy-preserving analytics, knowledge graphs, LLMs or multi-modal data integration.
• Track record of high-quality research outputs, technical delivery, grant/proposal development, team science and effective communication with academic, public health and technical stakeholders.

The ideal candidate will combine scientific credibility with practical implementation experience. They should be comfortable working across disciplines, from machine learning and genomics to public health and policy, and motivated to build AI-enabled genomic intelligence approaches that are rigorous, responsible and useful for real-world outbreak preparedness in Asia and beyond.