Applicants are welcome to apply for more than one position, but a separate application will be required for each position.
Open Job Opportunities – Quick Links
Click on a link below to be taken directly to the application. All hyperlinked positions below are currently accepting applications.
Faculty Jobs:
Assistant/Associate Professor of Biostatistics
Postdoc Jobs:
Postdoctoral Research Fellowship in Biostatistics – Dr. Rui Wang, Dr. Michael Hughes
Postdoctoral Research Position in Environmental Health and Biostatistics – Dr. Danielle Braun, Dr. Antonella Zanobetti
Postdoctoral Research Position in Data Science – Dr. Francesca Dominici
Postdoctoral Research Position in Statistical Genetics and Genomics – Dr. Xihong Lin
Postdoctoral Research Fellow for Statistical Methods in Population Health Disparities – Dr. Briana Stephenson
Postdoctoral Fellow – Dr. John Quackenbush
Postdoctoral Research Position in Biostatistics and Data Science – Dr. Rui Duan
Research Associate/ Research Scientist Jobs:
Research Associate/Research Scientist – Dr. Curtis Huttenhower
Research Associate – Bioinformatics Analyst and/or Trainer – Harvard Chan Bioinformatics Core (HBC)
Assistant/Associate Professor of Biostatistics
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 quantitative health science research in areas such as 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. Candidates with a broad range of research interests are welcome and encouraged to apply; particular attention will be paid to applicants whose areas of interest include methodological work in statistical and population genetics and/or causal inference. Responsibilities will include methodological and collaborative research, and teaching and supervision of graduate students.
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 statistics. 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.
Qualifications:
Qualified applicants will have a doctoral degree in biostatistics, statistics, mathematics, computer science, computational biology, epidemiology, or a related field. Candidates are required to have their doctoral degree at the time of application and two years or more of postdoctoral or equivalent research experience by the time of the appointment start date; academic rank will be determined in accordance with the successful candidate’s experience.
Particular attention will be paid to applicants whose areas of interest include methodological work in statistical and population genetics and/or causal inference. In addition to having a keen interest in the development and application of biostatistical methods and computation in 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
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Application Procedures:
To apply, visit https://academicpositions.harvard.edu/postings/12113 . For any questions, reach out to Trevor Bierig at biostatjrsearch@hsph.harvard.edu .
Research Associate – Bioinformatics Analyst and/or Trainer
Description:
– Cover letter, including why you think this position is a good fit for you.
– CV
– Sample publications
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11281
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Research Associate/Research Scientist
Description:
Basic Qualifications
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11352
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Fellow
Description:
We are seeking a candidate with expertise in computational and systems biology to work as part of a multidisciplinary team developing methods relevant to the study of genetics, gene regulatory networks, and the use of quantitative imaging data as biomarkers. Our goal is to use these methods to better understand the development, progression, and response to therapy. The successful applicant will work directly with Dr. John Quackenbush, but will be part of a community of researchers consisting of Dr. Quackenbush, Dr. Kimberly Glass, Dr. John Platig, and Dr. Camila Lopes-Ramos, and members of their research teams.
Basic Qualifications
A PhD in computational biology, biostatistics, applied mathematics, physics, biology, or related fields and demonstrated skill in methods and software development and the analysis of biological data are required.
Additional Qualifications
The ability to work as part of a large, integrated research team and strong verbal and written communication skills are essential. Previous work in cancer biology/cancer genomic data analysis is welcome but not required.
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11790
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Fellow for Statistical Methods in EHR-Based Studies
Description:
This is a postdoctoral position with the scientific goals being the development, implementation, and evaluation of statistical methods for causal inference based on electronic health records (EHR) data. The postdoctoral fellow will work with Drs. Haneuse, Mukherjee, and Wang in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, as well as their collaborators at Kaiser Permanente. The focus of the work will be on semi-parametric methods for handling missing data in EHR-based settings but may extend beyond, and will involve hands-on analysis of long-term outcomes following bariatric surgery.
Basic Qualifications
Doctoral degree in Biostatistics, Statistics, Computer Science, or a related field. Strong theoretical and programming skills (e.g., R, C++, Python, Julia) are required and experience with EHR data is a plus.
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11008
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Position in Biostatistics and Data Science
Description:
The Department of Biostatistics at Harvard TH Chan School of Public Health invites applications for a postdoctoral research fellow position focused on developing innovative statistical and machine-learning methods for analyzing and integrating various types of complex biomedical data (e.g., electronic health record data, claims data, biobanks). Areas of particular interest include (but not limited to): (1) genetic risk prediction for underrepresented populations; (2) distributed causal inference in clinical research networks; (3) statistical methods for high-dimensional data. The postdoctoral fellow will be supervised by Dr. Rui Duan at Harvard and will work closely with collaborators across multiple institutions. The appointment will be for a one-year contract with potential for renewal. The fellow will be expected to participate in methodological research leading to publications in top statistical and applied journals.
Basic Qualifications
1. Doctoral degree in Biostatistics, Statistics, Computer Science, or a related quantitative field.
2. Strong theoretical training in statistics or strong programing skills (R, Python, or C++) are desired.
Special Instructions:
Please submit:
- a cover letter
- a curriculum vitae
- one-page research statement
- one first author publication
- three references
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11807
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Position in Environmental Health and Biostatistics
The Department of Environmental Health together with the Biostatistics Department at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Fellow position for the analysis of large-scale environmental health data. The Postdoctoral Fellow will work with a multi-disciplinary team to investigate the impact of air pollution and other exposures on risk for and progression of Parkinson’s disease, and identify the complex interactions of individual-level, environmental and societal factors that lead to increased vulnerability in Parkinson’s disease.
The position will be under the joint supervision of Dr. Antonella Zanobetti in the Department of Environmental Health, and Dr. Danielle Braun in the Biostatistics Department. Applicants should have an interest in applying novel and state-of-the-art statistical and data science methods in environmental health. The specific position involves: (1) analysis of short- and long-term effects of air pollution and temperature on hospital admissions for Parkinson’s disease in Big Data applications using data from Medicaid and Medicare Services using traditional and novel causal inference approaches, (2) identification of vulnerable subpopulations using causal inference machine learning approaches, (3) estimation of exposure-response functions using causal inference approaches, (4) development of approaches to disentangle the effects of air pollution exposure from other multiple confounding factors (socio-economic (SES), neighborhood-level factors such as green space and noise), (5) development and application of statistical models, including causal inference methods.
Qualifications:
Doctoral degree in Biostatistics, Applied Statistics, Environmental Health, or related field.
Experience in analyzing real data, air pollution health studies, public health, strong programming skills, and strong statistical methods are preferred.
Excellent communication and writing skills desired.
The ideal candidate is an independent, solution-oriented thinker with a strong background in statistical methods and processing very large data sets, applying analytical rigor and driving toward actionable insights and novel solutions.
Duties and Responsibilities
The Post-doctoral Fellow will contribute to the effort of:
Data integration from different data sources
- Analyzing environmental health effects in big data
- Refine and improve statistical methods to disentangle the effects of air pollution exposure from other confounding factors by leveraging approaches for causal inference and correct for potential outcome misclassification and exposure error
- Apply machine learning methods to identify co-occurrence of individual-level, environmental, and societal factors that lead to increased vulnerability
- Collaborate with our biostatistics and data science group
- Writing scientific articles and research proposals
- Participate in weekly meetings with supervisors, reporting on work performed and suggesting additional analysis or modifications in current procedures
- Oversee the activities and mentor other research staff / students.
Basic Qualifications
PhD in Biostatistics, Applied Statistics, Environmental Health.
- Experience in air pollution health studies.
- Experience in handling very large spatial datasets.
- Experience in applied statistics and computational methods.
- Knowledge of R, SAS, and Python.
- Interest in open-source software, reproducibility and data management.
Additional Qualifications
- Familiarity with multiple data science tools and ability to learn new tools as required.
- Experience with version control systems, in particular Git and GitHub.
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11797
Your application package will include:
- a cover letter
- a curriculum vitae
- 1 page research statement
- at least three names for references
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Position in Data Science
Description:
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Fellow position for data science and/or statistical methods development to address questions of climate change and air pollution regulatory policy. Expertise in causal inference and machine learning is desirable but not necessary. The Postdoctoral Fellow will work with a multi-disciplinary team with expertise in statistical methods, epidemiology, climate science, and biomedical science as part of the NSAPH group, https://www.hsph.harvard.edu/nsaph/ . The position will be under the supervision of Dr. Francesca Dominici, Professor of Biostatistics and co-Director of the Harvard Data Science Initiative.
Duties and Responsibilities
The Post-doctoral Fellow will contribute to the effort of:
- Analyzing environmental health effects in big data
- Collaborate with our biostatistics and data science group
- Writing scientific articles and research proposals
Basic Qualifications
- PhD in Biostatistics, Statistics or Computer Science.
- Experience in handling very large spatial datasets.
- Experience in applied statistics and computational methods.
- Knowledge of R, and Python.
- Interest in open-source software, reproducibility and data management.
Additional Qualifications
- Familiarity with multiple data science tools and ability to learn new tools as required.
- Experience with version control systems, in particular Git and GitHub.
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11790
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Fellow in Biostatistics
Description:
Primary author on at least one publication in a leading peer-reviewed journal.
– Cover letter, including why you think this position is a good fit for you.
– CV
– Sample publications
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/10673
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Fellow for Statistical Methods in Population Health Disparities
Description:
The Department of Biostatistics at Harvard TH Chan School of Public Health invites applications for a Postdoctoral Research Fellowship focused on the development of statistical methods for population health disparities. The postdoctoral fellow will work with Dr. Briana Stephenson and collaborate with a multidisciplinary research team to develop innovative statistical and machine learning methods to address and identify bias and inequities in population health. Areas of interest include: identifying bias in healthcare access and delivery, statistical methods for high-dimensional exposures in minority populations, model-based clustering techniques for understudied populations, and survey sampling methodology for diverse population cohorts. Research applications will utilize data from cancer registries, national survey studies, and large prospective cohort studies. The postdoctoral fellow will develop their research and training agendas through formal mentorship, seminars, conferences, and an Individual Development Plan (IDP) to explore and identify the fellow’s professional needs and career objectives.
Basic Qualifications:
- Doctoral degree in Biostatistics, Applied Statistics, Computer Science, data science or related field
- Experience developing and implementing statistical methods
- Experience analyzing healthcare or population cohort study data
- Strong statistical programming skills (e.g. R, MATLAB, Python, C++, etc.)
- Strong oral and written communication skills
Additional Qualifications:
- Experience implementing Bayesian models
- Experience processing and analyzing large datasets
Special Instructions:
• Cover letter
• Curriculum vitae
• One-page research statement and/or one representative first author publication
• Two references
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/10252
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Fellow Positions in (i) Network Science and (ii) Biomedical Smartphone Research
Description:
The Onnela Lab in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health is seeking candidates with a Ph.D. in biostatistics, applied mathematics, statistical physics, computer science, or a related quantitative field for two-year Postdoctoral Research Fellow positions. These positions involve developing statistical methods, data analytic tools, and mathematical models for analyzing two different types of systems. In the first area, statistical network science, we develop methods that are at the intersection of statistical learning and network science with applications in social and biological networks. In the second area, smartphone-based digital phenotyping, we develop tools and methods for analyzing data collected by our smartphone platform. Our ongoing applied studies in this area involve diverse patient populations from neurology to psychiatry and oncology. The candidates can focus on one of these areas only or may work across both, depending on interests and expertise.
Qualifications:
Doctoral degree in biostatistics, computer science, applied mathematics, statistical physics, or a related quantitative field. Excellent programming skills in Python or similar language, as well as strong oral communication and writing skills are required.
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/8588
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
For more information about the lab, please visit https://www.hsph.harvard.edu/onnela-lab/.
Onnela Lab Postdoctoral Research Fellow Position Digital Phenotyping / Smartphone Data Research
Description:
Basic Qualifications
Doctoral degree in statistics, biostatistics, computer science, applied mathematics, or a related quantitative field. Excellent programming skills, as well as strong oral communication and writing skills are required.
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11794
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
For more information about the lab, please visit https://www.hsph.harvard.edu/onnela-lab/.
Onnela Lab Postdoctoral Research Fellow Position Network Science
Description:
The Onnela Lab in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health is seeking candidates with a Ph.D. in biostatistics, applied mathematics, statistical physics, computer science, or a related quantitative field for a two-year Postdoctoral Research Fellow position. This position, which is in the field of network science, involves developing new network community detection methods for administrative health data. Some of the goals of the project involve leveraging nodal covariates (attributes) as part of the community detection procedure and extending the applicability of these methods to longitudinal data. Understanding uncertainty in the detected community structure is also of interest in this project.
Basic Qualifications
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/11848
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Fellow/Research Associate Position in Biostatistics and Biomedical Informatics
Description:
A Postdoctoral Research Fellow or Research Associate position in biostatistics and biomedical informatics is available at Harvard T.H. Chan School of Public Health. The positions involve developing and applying statistical and computational methods for analysis of electronic health records (EHR) data including narrative data extracted via natural language processing, codified phenotype data as well as large scale genomic measurements. We seek an individual with strong statistical and computing backgrounds and who has expertise in statistical and machine learning methods for big data. The work will involve development and application of statistical and informatics methods and algorithm for analyzing EHR data.
Basic Qualifications:
Ph.D. in a quantitative field, e.g., statistics or biostatistics, computer sciences, strong quantitative research background, statistical and programming proficiency, as well as good written and oral communication skills.
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/10604
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Postdoctoral Research Position in Statistical Genetics and Genomics
Description:
Postdoctoral Research Fellow position in statistical genetics and genomics is available at Harvard T. H. Chan School of Public Health. This position involves developing and applying statistical and machine learning methods for analysis of high-throughput genetic and genomic data, including large scale Whole Genome Sequencing association studies, integrative analysis of genetic and genomic data, high-dimensional phenotype analysis, causal mediation analysis and Mendelian Randomization, Polygenic risk scores, and analysis of biobanks. We seek an individual with strong statistical, computing, and genetic backgrounds and who has expertise in statistical and computational methods for big data, statistical genetics and genomics. The work will involve both methodological research with department faculty and collaboration with subject matter researchers and investigators in large consortia.
Qualifications:
Ph.D. in a quantitative field, e.g., statistics or biostatistics, computer sciences, strong quantitative research background, statistical and programming proficiency, strong genetic knowledge, as well as good written and oral communication skills.
Application Procedures:
To apply for this position, submit your application through the Harvard ARIeS: Academic Recruiting Information eSystem at the following link:
https://academicpositions.harvard.edu/postings/10872
Additional Information:
Harvard University seeks to find, develop, promote, and retain the world’s best scholars. Harvard is an Affirmative Action/Equal Opportunity Employer. Applications from women and minority candidates are strongly encouraged.
Information on resources for career development and work/life balance at SPH can be found at: Career development and work/life balance.
Postdoctoral Research Position in Environmental Health and Biostatistics
The Department of Environmental Health together with the Biostatistics Department at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Fellow position for the analysis of large-scale environmental health data. The Postdoctoral Fellow will work with a multi-disciplinary team to investigate the impact of air pollution and other exposures on risk for and progression of Alzheimer’s disease and related dementias (AD/ADRD), and identify the complex interactions of individual-level, environmental and societal factors that lead to increased vulnerability in AD/ADRD.
The position will be under the supervision of Dr. Antonella Zanobetti in the Department of Environmental Health, and Drs. Francesca Dominici and Danielle Braun in the Biostatistics Department. Applicants should have an interest in applying novel and state-of-the-art statistical and data science methods in environmental health. The specific position involves: (1) analysis of short- and long-term effects of air pollution and temperature on hospital admissions for AD/ADRD in Big Data applications using two cohorts (Medicaid and Medicare enrollees in the continental US), (2) identification of vulnerable subpopulations, (3) exposure-response functions, (4) disentangle the effects of air pollution exposure from other multiple confounding factors (socio-economic (SES), neighborhood-level factors such as green space and noise), (5) development and application of statistical models, including causal inference methods.
Qualifications:
Doctoral degree in Biostatistics, Applied Statistics, Environmental Health, or related field.
Experience in analyzing real data, air pollution health studies, public health, strong programming skills, and strong statistical methods are preferred.
Excellent communication and writing skills desired.
The ideal candidate is an independent, solution-oriented thinker with a strong background in statistical methods and processing very large data sets, applying analytical rigor and driving toward actionable insights and novel solutions.
Duties and Responsibilities
The Post-doctoral Fellow will contribute to the effort of:
- Data integration from different data sources
- Analyzing environmental health effects in big data
- Refine and improve statistical methods to disentangle the effects of air pollution exposure from other confounding factors by leveraging approaches for causal inference and correct for potential outcome misclassification and exposure error
- Apply machine learning methods to identify co-occurrence of individual-level, environmental, and societal factors that lead to increased vulnerability
- Collaborate with our biostatistics and data science group
- Writing scientific articles and research proposals
- Participate in weekly meetings with supervisors, reporting on work performed and suggesting additional analysis or modifications in current procedures
- Oversee the activities and mentor other research staff / students.
Basic Qualifications
- PhD in Biostatistics, Applied Statistics, Environmental Health.
- Experience in air pollution health studies.
- Experience in handling very large spatial datasets.
- Experience in applied statistics and computational methods.
- Knowledge of R, SAS, and Python.
- Interest in open-source software, reproducibility and data management.
Additional Qualifications
- Familiarity with multiple data science tools and ability to learn new tools as required.
- Experience with version control systems, in particular Git and GitHub.
To apply
Please submit to Antonella Zanobetti at azanobet@hsph.harvard.edu.
- a cover letter
- a curriculum vitae
- 1 page research statement
- at least three names for references
Postdoctoral Position
The Haber Lab in the Department of Environmental Health at Harvard T.H. Chan School of Public Health has an opening for a highly motivated Postdoctoral Fellow. Projects will involve developing and applying computational approaches to investigate effects of environmental exposures on mammalian lungs, investigating downstream effects on asthma pathogenesis and exacerbations. The successful candidate will join an interdisciplinary team spanning the Chan School, Brigham & Women’s and Boston Children’s Hospitals and the Broad Institute of MIT and Harvard.
We seek an enthusiastic post-doctoral researcher to investigate the role of cellular heterogeneity in asthma using computational and systems biology methods. Projects in the lab examine the cellular and molecular effects of environmental exposures on the airways, and focus on analysis of high-dimensional ’omics data (particularly single-cell RNA sequencing) from clinical samples and mouse models of airway injury, inflammation and regeneration. Our group collaborates closely with clinical pulmonologists and immunologists to study mechanisms underlying both airway homeostasis and asthma pathogenesis.
How To Apply
Interested candidates should email a CV and research statement to Dr. Haber (ahaber@hsph.harvard.edu) or with any questions.
Requirements
• PhD or equivalent in computational biology, biostatistics, computer science, mathematics, epidemiology, or other quantitative field.
• Experience with computational genomics, statistical or epidemiological data analysis, preferably in R.
• Candidates holding a degree in biological/medical science are also welcome to apply if they have extensive background in computational or statistical work.
What you can expect
• Scientific and professional mentorship (1:1 meetings, weekly or as needed).
• Timely feedback on manuscripts, fellowships, faculty applications.
• Supportive lab culture focused on scientific rigor and constructive feedback.
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.