Job Description
The Saw Swee Hock School of Public Health at the National University of Singapore (NUS) seeks a highly motivated and skilled Research Fellow to join an interdisciplinary research projects using spatial and remote sensing data for surveillance for zoonotic and vector-borne diseases across Southeast Asia. This position offers the opportunity to work on ground-breaking research projects in the broad areas of environmental change, remote sensing, infectious disease surveillance, spatial epidemiology, machine learning and global health.
Key responsibilities:
- Conduct high quality research in epidemiology, remote sensing, machine learning and/or meta-analyses to identify impacts of environmental change on infectious disease dynamics and design targeted surveillance approaches;
- Develop and implement statistical methods, computational models and/ or machine learning algorithms to analyse environmental and infectious disease data;
- Collaborate with multidisciplinary and international teams of researchers, including epidemiologists, biologists, environmental scientists and policy makers;
- Present research findings at national and international conferences and publish results in peer-reviewed scientific journals;
- Support design of field studies in collaboration with international partners;
- Assist in the supervision and mentoring of graduate students and junior researchers; and
- Contribute to grant proposals and progress reports for funding agencies.
Review of applications will begin immediately and continue until the position is filled. The anticipated start date is October 2026, but this is negotiable. The initial appointment will be for one year, with the possibility of renewal based on performance.
NUS is an equal-opportunity employer committed to diversity and inclusion. We welcome applications from all qualified individuals, regardless of race, color, religion, gender, sexual orientation, age, national origin, or disability.
Candidates interested in this job opportunity are encouraged to contact A/Prof Kimberly Fornace (kfornace@nus.edu.sg) by email.
Qualifications
- A PhD in a relevant field such as epidemiology, geography, computer science or a related discipline;
- Strong background in any one of machine learning, infectious disease modelling, epidemiology, quantitative environmental science or ecology, remote sensing or a related field;
- Proficiency in programming languages such as R or Python;
- Experience with machine learning or statistical methods applied to public health and/ or environmental challenges;
- Interest in climate and environmental change and infectious disease dynamics;
- Excellent written and verbal communication skills, including presenting complex concepts to diverse audiences;
- Demonstrated ability to work effectively in a multidisciplinary team and independently; and
- Track record of publications in peer-reviewed journals.
