Research Assistant (Cancer Epidemiology And Prediction)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal. NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Assistant-%28Cancer-Epidemiology-and-Prediction%29/33772-en_GB/ We regret that only shortlisted candidates will be notified. Job Description Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health: Research Assistant (Cancer Epidemiology and Prediction) The Saw Swee Hock School of Public Health (SSHSPH) 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 Requirements: 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. The grade of appointment will be accorded based on candidate’s academic qualifications and years of relevant experience. Applicants should send the following documents during application: Cover letter highlighting career goals and relevant experience Curriculum Vitae, containing details of three named referees Please note that only shortlisted candidates will be contacted.