School: Harvard T.H. Chan School of Public Health Department/Area: Global Health and Population (GHP) Position Description: Dr. Menzies' Research Lab in the Department of Global Health and Population at the Harvard T.H. Chan School of Public Health is seeking candidates for an immediate opening for a one-year post-doctoral fellow position in COVID-19 policy modeling. The lab undertakes a range of research investigating the health impact, costs, and cost-effectiveness of infectious disease treatment and prevention programs in the United States and in low resource settings, using mechanistic and statistical computer modelling. The research involves close collaboration with the Centers for Disease Control and Prevention for US-based TB research, and also with the Gates Foundation, WHO, and the Global Fund Against AIDS, TB, and Malaria. The successful candidate will work closely with Dr. Menzies to develop mathematical models and conduct analyses to inform COVID-19 public health decision making within the United States. The candidate may also have opportunities to collaborate on additional projects depending on interests and expertise. Basic Qualifications: Doctoral degree in health policy, epidemiology, health data science, biostatistics, or a related field. The ideal candidate will have demonstrated excellence in research in one of more of the fields mentioned above. Desired skills include: Contact Information: Informal inquiries are welcome. Administrative questions regarding this position can be sent to Cindy Imai at cimai@hsph.harvard.edu. Scientific questions regarding this position can be sent to Nicolas Menzies at nmenzies@hsph.harvard.edu. Contact Email: cimai@hsph.harvard.edu. Special Instructions: To apply, please submit a cover letter, CV, and three letters of reference through the ARIES application system. Equal Opportunity Employer: We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
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