Duke Nus Graduate Medical School jobs in Singapore

Job Description

The Signature Research Programme in Health Services Research & Population Health (“HSRPH”) in Duke-NUS Medical School is a centre of academic excellence for research and education of health systems and services for the population in Singapore and beyond. Our research methods include implementation science, health economics, decision science, survey/qualitative research, quantitative medicine, epidemiology, and data science. The population health outcomes programme Duke-NUS has established provides a framework for evaluating and improving healthcare delivery systems nationwide. We are also developing a range of educational activities for healthcare professionals including short courses, graduate certificates, Masters course and PhD programmes.

We are seeking a Research Assistant to join our team at HSRPH. The responsibilities includes:-

• Perform data cleaning, handling and analysis
• Assist in drafting research abstracts and manuscripts
• Assist in preparation of PowerPoint presentations to various stakeholders
• Conduct literature reviews to evaluate the potential value of health innovations
• Assist in preparation of evaluation reports for the health economics team
• Extract data from existing databases and published literature
• Support project management activities across different stages of research, including development of study objectives, study design, data extraction, data collation, statistical analysis, modelling, and stakeholder engagement, under guidance from senior team members .
• Support secretariat and administrative functions pertaining to the health economics team at HSRPH

Qualifications

• Possess a Bachelor’s degree in any discipline. Candidate with training in statistics, economics, data science, public health, epidemiology, or related disciplines, and with strong quantitative skills is preferred.
• Adept at working with data, including processing raw data to visualise trends and deciphering patterns and ability to handle large data sets
• Experience in various quantitative research methods is preferred.
• Experience with R programming language, and willing to learn more applications
• Excellent verbal and written communication and interpersonal skills
• Ability to work in a team, with dynamic reporting lines and multiple stakeholders
• Ability to work independently with limited supervision.
• A highly motivated and action-oriented individual.

We regret that only shortlisted candidates will be notified.