Job Description

The Yong Yoo Lin School of Medicine (YYLSoM) at the National University of Singapore (NUS) is recruiting a full-time, experienced research assistant to develop and evaluate breast cancer risk prediction models in cancer study in Singapore. This role offers the opportunity to contribute to cutting-edge research, collaborate with a team of experts, and work on cancer research.

Job scope:

• To undertake high-quality research, including contributing to drafting major grant proposals and/or leading in drafting small grant proposals;
• To develop and extend cancer risk prediction models;
• To support ethics applications;
• To contribute to peer-reviewed publications and other outputs, including as lead author;
• Contribute to drafting grant proposals and co-authoring publications.
• To review the latest research on breast cancer risk, prognosis and survivorship studies.
• To disseminate research findings through presentations at regional and international conferences.
• To contribute to the broader research community through journal and grant reviews.
• To participate in mandatory NUS training and keep abreast of advancements in research methods.

Qualifications

• Bachelor's or Master's degree in Statistics, Biostatistics, Engineering, Economics, Pharmacy, Public Health, or Computer Science.
• Experience with quantitative research, preferably related to cancer prediction models.
• Strong analytical and data management skills using electronic medical records and observational data.
• Experience with statistical software (e.g., R) and programming languages (e.g., R, C/C++, Python).
• Excellent written and verbal communication skills.
• Ability to work independently and collaboratively within a team.
• Strong organizational skills and time management.

The following knowledge and experience would be advantageous:

• Experience contributing to research grant applications.
• Experience with analysis of data from epidemiological study designs.
• Experience with data analysis using statistical inference techniques.
• Experience with health economic evaluations.
• Experience with parallel and/or high-performance computing.

Competencies