Research Associate is required for concurrent research projects in the laboratory of Assistant Professor Lim Jue Tao.

This project is led by the Lee Kong Chian School of Medicine (LKCMed), Nanyang Technological University. This position will offer a multidisciplinary and unique collaborative environment with diverse learning opportunities. The successful applicant will work as part of a growing and energetic team investigating health at the interface of climate change, clinical epidemiology, and infectious diseases in local and international contexts. The successful candidate will work together with the senior researcher to develop statistical learning and machine learning based models to understand climate change impact on burdens and management of infectious diseases and chronic diseases.

Key Responsibilities:

• Develop models to understand how climate change will impact burdens and management of infectious diseases and chronic diseases

• Undertake literature reviews

• Lead the analysis of global climate model data and large-scale population level data using statistical learning and machine learning techniques

• Lead the writing of reports, presentations, and publications of analysis findings in peer-reviewed journals

• Generate research questions

• Collaborate with researchers in other national and international institutions

Competencies and Qualification Requirements:

• Masters or equivalent in a quantitative discipline (e.g. statistics, biostatistics, computer science, data science, biomedical engineering, or similar). Post-graduate qualifications in other quantitative disciplines are welcome to apply.

• Background and research experience in epidemiology, public health, or life sciences

• A strong interest in infectious diseases and health data science

• Ability to communicate findings effectively

• Ability to work among a diverse and large team of researchers

• Well- organized and has an eye for details

• Possess good written and verbal communication skills

• Possess the ability to work effectively in teams

• Have strong proficiency in Statistical software (R)

It would be desirable for the candidate to:

• Have familiarity in other programming languages, for example Python/C/C++

• Have a strong interest in learning quantitative skills