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Harvard University Assistant/Associate Professor of Biostatistics in Cambridge, Massachusetts

Details

Title Assistant/Associate Professor of Biostatistics

School Harvard T.H. Chan School of Public Health

Department/Area Biostatistics; Environmental Health

Position Description

The Department of Biostatistics at the Harvard T.H. Chan School of Public Health seeks candidates to fill a tenure-track faculty position at the assistant or associate professor level. We seek candidates with backgrounds in biostatistical theory and methods and/or machine learning and data science, with demonstrated expertise in the development of new methods as well as a record of collaborative research. We are particularly interested in applicants with demonstrated research interests in any area relevant to the environmental health and climate sciences. Responsibilities will include methodological and collaborative research, and teaching and supervision of graduate students. This faculty member would hold a primary appointment in the Department of Biostatistics, and a secondary appointment in the Department of Environmental Health.

The Department of Biostatistics offers an exceptional environment to pursue research and education in biostatistics while being at the forefront of efforts to benefit the health of populations worldwide. Our faculty are leaders in the development of methods for the design and analysis of clinical trials and observational studies, missing data, causal inference, precision health, network analysis, computational and systems biology, microbiome analysis, statistical genetics and genomics, neurostatistics, statistical and machine learning methods, and environmental biostatistics. Our innovative approaches to the analysis of massive health-related data are strengthened by a deep foundation in theory and application. The department prides itself on having strong mentoring and a supportive environment for assistant and associate professors. Our unique and diverse community provides unparalleled collaborative opportunities with academic departments across Harvard, the Dana-Farber Cancer Institute, and other world-class Harvard-affiliated hospitals.

Basic Qualifications

Qualified applicants will have a doctoral degree in biostatistics, statistics, mathematics, computer science, computational biology, or a related field. Candidates are required to have their doctoral degree at the time of application. Academic rank will be determined in accordance with the successful candidate’s experience.

Additional Qualifications

In addition to having a keen interest in the development and application of biostatistical methods and computation in the environmental health sciences, candidates should be enthusiastic about teaching, training, and mentorship through our graduate programs. Ideal candidates should also be committed to fostering principles of diversity, inclusion, and belonging throughout their research and teaching activities.

Special Instructions

Applications must be received by December 1, 2024. Four references will be requested of candidates at the assistant professor rank, and six references will be requested of candidates at the associate professor rank. Candidates of the Assistant Professor rank should provide two publications, while Associate Professor candidates should provide 5 publications.

For guidance on the Service Statement, click to learn more about the Harvard Chan School Principles of Citizenship (https://www.hsph.harvard.edu/about/) .

Contact Information

Trevor Bierig

Contact Email biostatjrsearch@hsph.harvard.edu

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 4

Maximum Number of References Allowed 6

Keywords

Biostatistics; data science; machine learning; environmental health; climate research

Supplemental Questions

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