美國哈佛大學(xué)陳曾熙公共衛(wèi)生學(xué)院2023年招聘博士后（流行病學(xué)方向）

哈佛大學(xué)（Harvard University），簡稱“哈佛”，位于美國馬薩諸塞州波士頓都市區(qū)劍橋市，一所頂尖私立研究型大學(xué)，常春藤盟校，全球大學(xué)校長論壇、全球大學(xué)高研院聯(lián)盟成員。

Postdoctoral Research Fellow

Harvard University

Title Postdoctoral Research Fellow

School Harvard T.H. Chan School of Public Health Department/Area Epidemiology Position Description

The Department of Epidemiology at the Harvard T.H. Chan School of Public Health studies the frequency, distribution, and determinants of disease in humans, a fundamental science of public health. In addition to pursuing ground-breaking global research initiatives, we educate and prepare future medical leaders and practitioners as part of our mission to ignite positive changes in the quality of health across the world.

The Center for Communicable Disease Dynamics (CCDD), in the Department of Epidemiology, is a key center for research and policy analysis on SARS-CoV-2 and has been at the forefront of the COVID-19 response. CCDD's team, comprised of faculty, researchers, postdocs, and graduate students, focuses on innovative modeling techniques, interdisciplinary methods, and data analysis to understand infectious disease dynamics.

To learn more about the CCDD and the affiliated labs, please see https: // ccdd.hsph.harvard.edu/.

The postdoctoral fellow would support Marc Lipsitch's lab primarily working on developing a Bayesian model of antibody kinetics following COVID-19 vaccination and infection (see below).

The postdoctoral research fellow, working with Dr. Lipsitch's lab and CCDD, will lead the development of a Bayesian model of antibody kinetics following COVID-19 vaccination and infection. Model development will be based on data from a large cohort of health care workers at Sheba Medical Center, Israel (1, 2), in a collaboration with the PI of that study, Dr. Gili Regev-Yochay, as well as Dr. Noam Barda (Sheba), and Dr. Nima Hejazi, Department of Biostatistics (Harvard T.H. Chan School of Public Health). The purpose of the Bayesian model is to provide estimates for the time course of levels of various immune markers (e.g. binding and neutralizing antibodies) such that with one or a few timepoint measurements, these markers can be predicted (with associated uncertainty) at other unmeasured timepoints. Application of the model will include estimating correlates of protection for time-varying (rather than fixed-timepoint) immune marker measurements.

1. Regev-Yochay G, Gonen T, Gilboa M, et al. Efficacy of a Fourth Dose of Covid-19 mRNA Vaccine against Omicron. N Engl J Med. 2022;386(14):1377-80. doi:10.1056/NEJMc2202542 2. Bergwerk M, Gonen T, Lustig Y, et al. Covid-19 Breakthrough Infections in Vaccinated Health Care Workers. N Engl J Med. 2021. doi:10.1056/NEJMoa2109072

Basic Qualifications

Education Requirements

A doctoral degree in statistics, data science, biostatistics, epidemiology, or a related field

Experience Requirements

Research experience and success in publishing papers

Technical Requirements

Bayesian model fitting and causal inference

Strong quantitative, analytical, and writing skills

Additional Qualifications

Preferred Experience and Skills Requirements

Familiarity with infectious diseases clinical practice, public health practice, surveillance strategies, and other related fields is a plus

Strong quantitative/statistical and/or coding skills and be

Proficiency in a common programming language such as Python or R

Strong communication skills (written and verbal), takes initiative, self-directed, time management

Highly creative, ability to work independently and as part of a team, and excitement to work in a collaborative environment and within the larger research ecosystems

Additional Information: Per university guidelines, postdoc appointments are considered to be on-campus, full-time positions. Per university payroll tax guidelines all applicants must reside in an acceptable payroll states or be willing to relocate to: Massachusetts, New Hampshire, Rhode Island, Connecticut, Maryland, Vermont or New York.

Special Instructions Contact Information

For additional questions about the position, please contact Laurie Coe, Director of Research and Administration, CCDD.

Contact Email lcoe@hsph.harvard.edu Equal Opportunity Employer

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