Postdoctoral Research Fellow (HIV Epidemiology and Modelling)

Harvard University; Massachusetts; On-site; USD 70,000 - 77,000; Full time

Job summary

Harvard University in Massachusetts is seeking a Postdoctoral Research Fellow to lead mathematical modelling and statistical analyses for NIH-funded studies on HIV epidemiology and transmission dynamics. The role is based in the Harvard T.H.

Chan School of Public Health with the Center for Communicable Disease Dynamics. Successful candidates have a PhD in a quantitative field and strong programming skills (R/Python/C++), with experience in population-based data and epidemiology.

Qualifications

• PhD in infectious disease epidemiology, biostatistics, or related quantitative field.
• Experience with population-based data analysis.
• Experience with epidemiology and transmission dynamics.
• Proficiency in at least one scientific programming language (R, Python, or C++).

Responsibilities

• Lead mathematical modelling and statistical analyses for NIH-funded studies.
• Collaborate with international partners including RCCS, RHSP, and Johns Hopkins.

Skills

C++ programming

R programming

Python programming

Bayesian modelling

Data analysis

Education

PhD in Infectious Disease Epidemiology or related quantitative discipline

Tools

R

Python

C++

Job description

Postdoctoral Research Fellow (HIV Epidemiology and Modelling)

Title Postdoctoral Research Fellow (HIV Epidemiology and Modelling)

School

School Harvard T.H. Chan School of Public Health

Department/Area

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. We seek one Postdoctoral Research Fellow to lead mathematical modelling and statistical analyses as part of two NIH-funded studies (IN- STEP and SENTINEL) with the Rakai Community Cohort Study (RCCS) in south-central Uganda. Both projects are collaborations with the Rakai Health Sciences Program (RHSP) and Johns Hopkins University, and other study team partners. The Postdoctoral Fellow will have primary responsibility for only one project aligned with skills and interests, but is expected work collaboratively with team other members across both projects where interests overlap, along with collaboration with other modeling teams at the Gates Foundation and Imperial College London. Successful candidates for the Postdoctoral Fellow role will have completed a doctoral degree in quantitative discipline, strong technical and analytical skills, demonstrated aptitude for and experience with academic research, and interest in developing a research portfolio in infectious disease dynamics with focus in HIV and STI control. The Postdoctoral Fellow will be based in the Center for Communicable Disease Dynamics in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health. They will be a member of the HIV Inference Group, a geographically distributed and substantively diverse research team led by Dr. Jeff Imai-Eaton. Both projects are close collaborations with the Rakai Health Sciences Program (Dr. Godfrey Kigozi, Dr. Ronald Galiwango, and colleagues) and Johns Hopkins University (Dr. M. Kate Grabowski and Dr. Larry Chang). Please read below for additional information about the study and the two projects. As mentioned above, the Postdoctoral Research Fellow will take primary responsibility for one project and collaborate on the other.

The Rakai Community Cohort Study

The RCCS, run by the Rakai Health Sciences Program, is one of the longest-running and largest population-based HIV surveillance cohorts in Africa. Since 1994, the RCCS has enumerated and surveyed all residents aged 15 years and older in about 40 communities in south-central Uganda roughly every two years. Each round collects detailed sociodemographic, behavioural, and health-service data, along with HIV and other laboratory testing, from about 20,000 participants. Biospecimens have been collected from more than 300,000 individuals. Over four decades, research in Rakai has shaped the global HIV response. It established the relationship between viral load and heterosexual HIV transmission, showed through randomized trials that medical male circumcision reduces HIV acquisition in men, documented the impact of combination HIV prevention and treatment on population-wide HIV incidence declines, and has pioneered the use of population-based HIV phylogenetics to unravel HIV transmission networks. The RCCS is now uniquely positioned to map the "end game" of a generalized HIV epidemic. As incidence falls, it can show where remaining infections occur, which interventions work, and what threatens progress toward epidemic control.

Project 1: IN- STEP

IN- STEP (Integrated Female Sexually Transmitted Infection Testing for HIV Epidemic Control through PrEP) is an individually randomized effectiveness-implementation trial nested within the RCCS to assess whether integrated STI testing increases uptake and continuation of HIV pre-exposure prophylaxis (PrEP) and improves STI outcomes, hypothesized through multiple pathways including leveraging ongoing STI transmission for efficient network-targeted interventions and STI diagnosis supporting enhanced individual motivation for HIV prevention. As part of IN- STEP, the HSPH team will lead development of a transmission modelling framework that simulates simultaneous transmission of HIV and multiple curable STIs within the same sexual networks and test apply the model to test network-science approaches to HIV and STI control. The model will be developed in collaboration with professional research simulation platform developers at the Gates Foundation Institute for Disease Modeling, and calibrated to empirical data from the RCCS and IN- STEP.

Project 2: SENTINEL

SENTINEL leverages longitudinal epidemiologic data and HIV genomic sequencing collected in the RCCS to address critical questions about how HIV infection dynamics in eastern and southern Africa evolve as incidence falls and assess emerging threats to HIV epidemic control. The HSPH team will lead longitudinal statistical analysis and mathematical modelling to unpack current transmission dynamics, sources of infection, whether new infections and transmission are increasingly concentrated in key populations or other identifiable subgroups, and project the impact of alternative HIV prevention and treatment strategies in the low-incidence era. Results will be applied to guide regional program strategies and surveillance priorities.

Basic Qualifications

Education Requirements

• PhD in Infectious Disease Epidemiology, Epidemiology, Biostatistics, Statistics, Population Biology, Demography, or a related quantitative discipline

Experience & Skills Requirements

• Research experience in analysis of population-based cohort, trial, surveillance, or population health data
• Research experience in infectious disease epidemiology, transmission dynamics, and/or mathematical modelling
• Experience with applied statistical modelling, such as regression, longitudinal, hierarchical, or Bayesian models
• Strong quantitative background, with demonstrated ability to program in one or more scientific programming languages, e.g. R, Python, C++

Additional Qualifications

Preferred Experience

• Knowledge of HIV and/or STI epidemiology and prevention in eastern and southern Africa
• Experience analysing longitudinal cohort data or randomized trial data
• Experience working multidisciplinary collaborative teams in hybrid research environments
• For SENTINEL: familiarity with HIV phylogenetics or pathogen genomic data, or with incidence estimation methods
• For IN- STEP: experience with agent-based or network modelling of infectious disease transmission

Preferred Skills

• Experience with modern Bayesian computing and probabilistic modelling tools such as Stan, TMB, INLA, or PyMC
• Experience with causal inference methods, multi-level models, or transmission-dynamic models
• Experience with reproducible research and team science workflows (e.g. Git/GitHub)
• Proven research skills and a record of first-author publications
• Excellent verbal and written communication skills, including the ability to communicate technical results to non-specialist and policy audiences
• Ability to work independently and as part of a larger, interdisciplinary, and geographically distributed team
• Comfort operating with ambiguity and working collaboratively in diverse cultural contexts
• Willingness and ability to travel internationally, including to Uganda

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 state or be willing to relocate to Massachusetts, New Hampshire, Rhode Island, Maine, Connecticut, Maryland, Vermont, New York, California, New Jersey, Virginia, Washington, Georgia, or Illinois.

Contact Information

For additional questions about the position, please contact Jeffrey Imai-Eaton, PhD, Associate Professor of Epidemiologyin the Department of Epidemiology.

Salary Range

$70,000 - $77,000 Compensation will be based on post-graduate ( PGY ) experience

Minimum Number of References Required 2

Maximum Number of References Allowed 2

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes. Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy. Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.

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