Research Assistant/Associate [LKCMedicine], Opportunity at NTU Novena Campus, Singapore.

This project, led by the Lee Kong Chian School of Medicine (LKCMed), Nanyang Technological University, will be conducted in collaboration with the Saw Swee Hock School of Public Health (SSHSPH), National University of Singapore, the Environmental Health Institute, National Environment Agency, Singapore and the Ministry of Health, Singapore will offer a multidisciplinary and unique collaborative environment with diverse learning opportunities. The successful applicant(s) will work as part of a growing and energetic team investigating health at the interface of environment, climate change and infectious diseases in local and international contexts. The successful candidates will work together with the PI to: (1) developing models to understand the epidemic potential and instantaneous transmissibility of infectious diseases (2) developing models to understand the medium to long-term burden of infectious diseases across large spatial scales

Job responsibilities:

• Undertaking literature reviews,
• Leading on analysing data using statistical and data science techniques,
• Leading on writing reports, presentations, and publication of results and findings in peer-reviewed journal,
• Generating research questions
• Collaborating with researchers in other national and international institutions

Skills and qualifications:

• Bachelor, Master degree or equivalent in a related discipline. I.e statistics/epidemiology/data science. Post-graduate qualifications in other quantitative disciplines are welcome to apply
• Background and research experience in epidemiology
• A strong interest in infectious diseases, spatial statistics and health data science
• Ability to communicate findings effectively
• Ability to work in a diverse and large team of researchers
• well- organized and has an eye for detail.
• Possess good written and verbal communication skills,
• Have good proficiency in other programming languages, for example Python/C/C++
• Have a strong interest in learning quantitative skills
• Possess the ability to work effectively in teams
• Have good proficiency in Statistical software (R)