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Form of workPosition
Details
Title
Postdoctoral Research Fellow in Biostatisticaland Causal Machine Learning Methods
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
Position Description
A Postdoctoral Research Fellowship position is available in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. The scientific goals of this position are the development, implementation, and evaluation of biostatistical methods for causal inference and Machine Learning, primarily as applied in the study of infectious diseases, including COVID-19 and HIV/AIDS. The successful postdoctoral candidate will be primarily supervised by and work closely with Dr. Nima Hejazi-with opportunities for co-supervision and co-mentorship from Drs. Sebastien Haneuse, Michael Hughes, and Xihong Lin-as well as collaborators at the Massachusetts General Hospital Biostatistics Center and the Fred Hutchinson Cancer Center. The scope of work will focus on non- and semi-parametric inferential statistical methods for applications related to COVID-19 (including its post-acute sequelae-"long COVID"), HIV/AIDS, and other high-impact infectious diseases. Specific research areas include the detection of effect modification and subgroup profiling using clinical histories, causal inference for effects of time-varying interventions in observational studies, and Causal mediation analysis as applied to studies evaluating the efficacy of candidate preventive/therapeutic agents. The postdoctoral fellow will have opportunities for both methodological research and close collaboration with subject matter experts.
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health offers an unparalleled environment in which to pursue research and education in (bio)statistical science while being at the forefront of efforts to benefit the health of populations worldwide.
Basic Qualifications
Doctoral degree in Biostatistics, Statistics, Computer Science, Epidemiology, or a related quantitative field and with expertise in advanced statistical theory, causal inference and/or Machine Learning. Strong programming skills (e.g., R, C++, Python, Julia) are required and prior experience with infectious disease applications is a plus. Excellent written and verbal communication skills, and ability to work collaboratively and independently, are expected.
Additional Qualifications
Special Instructions
Contact Information
For questions about this position, email biostat_postdoc@hsph.harvard.edu .
Contact Email
biostat_postdoc@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
2
Maximum Number of References Allowed
4
Keywords
Details
Title
Postdoctoral Research Fellow in Biostatisticaland Causal Machine Learning Methods
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
Position Description
A Postdoctoral Research Fellowship position is available in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. The scientific goals of this position are the development, implementation, and evaluation of biostatistical methods for causal inference and Machine Learning, primarily as applied in the study of infectious diseases, including COVID-19 and HIV/AIDS. The successful postdoctoral candidate will be primarily supervised by and work closely with Dr. Nima Hejazi-with opportunities for co-supervision and co-mentorship from Drs. Sebastien Haneuse, Michael Hughes, and Xihong Lin-as well as collaborators at the Massachusetts General Hospital Biostatistics Center and the Fred Hutchinson Cancer Center. The scope of work will focus on non- and semi-parametric inferential statistical methods for applications related to COVID-19 (including its post-acute sequelae-"long COVID"), HIV/AIDS, and other high-impact infectious diseases. Specific research areas include the detection of effect modification and subgroup profiling using clinical histories, causal inference for effects of time-varying interventions in observational studies, and Causal mediation analysis as applied to studies evaluating the efficacy of candidate preventive/therapeutic agents. The postdoctoral fellow will have opportunities for both methodological research and close collaboration with subject matter experts.
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health offers an unparalleled environment in which to pursue research and education in (bio)statistical science while being at the forefront of efforts to benefit the health of populations worldwide.
Basic Qualifications
Doctoral degree in Biostatistics, Statistics, Computer Science, Epidemiology, or a related quantitative field and with expertise in advanced statistical theory, causal inference and/or Machine Learning. Strong programming skills (e.g., R, C++, Python, Julia) are required and prior experience with infectious disease applications is a plus. Excellent written and verbal communication skills, and ability to work collaboratively and independently, are expected.
Additional Qualifications
Special Instructions
Contact Information
For questions about this position, email biostat_postdoc@hsph.harvard.edu .
Contact Email
biostat_postdoc@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
2
Maximum Number of References Allowed
4
Keywords
