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Post-Doctoral Research Fellow, Mathematical Modeler

Cures Start Here. At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. Fred Hutch’s pioneering work in bone marrow transplantation led to the development of immunotherapy, which harnesses the power of the immune system to treat cancer. An independent, nonprofit research institute based in Seattle, Fred Hutch houses the nation’s first cancer prevention research program, as well as the clinical coordinating center of the Women’s Health Initiative and the international headquarters of the HIV Vaccine Trials Network. Careers Start Here.

At Fred Hutch, we believe that the innovation, collaboration, and rigor that result from diversity and inclusion are critical to our mission of eliminating cancer and related diseases. We seek employees who bring different and innovative ways of seeing the world and solving problems. Fred Hutch is in pursuit of becoming an antiracist organization. We are committed to ensuring that all candidates hired share our commitment to diversity, antiracism, and inclusion.


Responsibilities

A Postdoctoral Research Fellow position with focus on development, refinement, and utilization of different classes of epidemiological transmission models, is available in the Vaccine and Infectious Disease Division at the Fred Hutchinson Cancer Research Center. Applicant will be joining a dynamic multi-disciplinary group to work on methods for effective model configuration and calibration using data collected by the COVID-19 Prevention Trials Network (CoVPN) as a basis for the methodological work. Among the key objectives of the modeling work are improving the understanding of SARS-CoV-2 transmission and COVID-19 disease dynamics, informing effective public health strategies, and guiding rational development of new preventive interventions, including vaccination programs. The position will require a substantial amount of programming and computation, good organization and communication skills.
 
To find out more, please visit: HPTN Modelling Centre and COVID-19 Model
 
This is a full-time position with salary based on NIH scale + excellent benefits. This position will remain open until filled.


Qualifications

Suitable applicants should have a PhD or equivalent degree in statistics/biostatistics, applied mathematics, infectious disease epidemiology, population biology, theoretical ecology or a similarly quantitative discipline. Research experience in mathematical modeling of biological systems, including but not limited to modeling infectious disease transmission and population dynamics, and experience with reviewing/analyzing scientific literature is required.

Experience with Bayesian statistical methods of model calibration or longitudinal data analysis is desired.

A statement describing your commitment and contributions toward greater diversity, equity, inclusion, and anti-racism in your career or that will be made through work at Fred Hutch is requested of all finalists.


Our Commitment to Diversity

We are proud to be an Equal Employment Opportunity (EEO) and Vietnam Era Veterans Readjustment Assistance Act (VEVRAA) Employer. We are committed to cultivating a workplace in which diverse perspectives and experiences are welcomed and respected. We do not discriminate on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability (physical or mental), marital or veteran status, genetic information, sexual orientation, gender identity, political ideology, or membership in any other legally protected class. We are an Affirmative Action employer. We encourage individuals with diverse backgrounds to apply and desire priority referrals of protected veterans. If due to a disability you need assistance/and or a reasonable accommodation during the application or recruiting process, please send a request to our Employee Services Center at hrops@fredhutch.org or by calling 206-667-4700.