Postdoctoral Research Fellow - Epidemiology
Postdoctoral Research Fellow
School Harvard T.H. Chan School of Public Health
Dr. William Hanage, PhD, Associate Professor in the Center for Communicable Disease Dynamics in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health is seeking a creative and motivated Postdoctoral Researcher with skills in theoretical/computational biology to develop and test models of the transmission and evolution of infectious disease. Dr. Hanage’s lab combines genomic data and theoretical approaches to improve our understanding of pathogen evolution and epidemiology. His lab has made pivotal contributions to our understanding of the response of pneumococci to vaccination, the spread of drug resistance in multiple pathogens, and the role of horizontal gene transfer in disseminating genetic innovation and speciation. He has also shown the importance of accounting for genetic variation within infections when reconstructing transmission networks from pathogen genetic data.
The successful candidate will work closely with Dr. Hanage and staff in the multidisciplinary research team based in CCDD where they will have the opportunity for professional development through interaction with other scientists applying bioinformatic, epidemiological and epidemic modeling approaches to viral, bacterial, and protozoan pathogens. They will also have opportunities to interact with leaders of bacterial evolution and infectious disease epidemiology at this institution and abroad. The fellow will have access to a wealth of resources including high-quality genomic and epidemiological data, a cutting-edge computing facility, robust analytical pipelines, the most recent sequencing and laboratory technology, and research expertise in genomics, mathematics, and computer science. The positions are funded through May 2022 with the potential for renewal.
Current projects include:
- Using short kmers to identify pathogen strains from genomic data in real time (Collaboration with Michael Baym at Harvard Medical School).
- Population genomics of bacterial pathogens including (but not limited to) Streptococcus pneumoniae, Staphylococcus aureus and Mycobacterium tuberculosis.
- Examining the pressures leading to serotype replacement in the pneumococcus following vaccination.
- Development of novel methods to detect transmission by examining polymorphisms within infections.
- Predicting evolution using models of frequency dependent selection on the accessory genome (Collaboration with Marc Lipsitch).
- Contributions to COVID-19 pandemic response where appropriate skills can be readily applied to an urgent problem.
- Doctoral qualification in Infectious Disease Epidemiology, population genetics, ecology, evolutionary biology or a related field. Successful candidates will have strong quantitative/statistical and/or coding skills and be proficient in a common language such as Perl, Python or R. Experience with dynamical, ecological and epidemiological or population genetic models of infectious disease is useful. Successful candidates will be able to think creatively and innovate new approaches to integrate relevant data streams such as complex genomic and ecological data, antibiotic resistance, clinical data, etc.
- Significant research experience and success in publishing papers.
- Strong quantitative, analytical and writing skills
- Highly creative, ability to work independently and part of a team, and excited to work in a collaborative environment within the Harvard School of Public Health, and within the larger research ecosystems that Harvard and Boston have to offer.
Interested candidates should contact Bill Hanage at firstname.lastname@example.org.
Inquiries or requests for more information can additionally be sent to email@example.com along with a CV.
Contact Email firstname.lastname@example.org
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, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
Minimum Number of References Required 3
Maximum Number of References Allowed 3