Academic Position Openings

Applicants are welcome to apply for more than one position, but a separate application will be required for each position.

Research Associate – Bioinformatics Trainer and Analyst

Description:
The Harvard Chan Bioinformatics Core is excited to expand our bioinformatics training program as part of a new collaboration with the Dana-Farber / Harvard Cancer Center (DF/HCC). We are looking for a bioinformatician to join our team in our efforts to provide education and analytical support to the Harvard community. The ideal candidate is enthusiastic about teaching (as demonstrated by their teaching experience), enjoys working in a collaborative environment, and has a background in high-throughput data analysis, specifically for next-generation sequencing (NGS) data. This role provides a unique and rewarding opportunity to train and support world-class researchers making a profound impact on human health.

Qualifications:
You have a background in cancer biology, biomedical or quantitative science and a strong interest in helping biomedical researchers. You have experience with NGS data analysis and enjoy teaching. You thrive on scientific challenges, love sharing knowledge, and enjoy working both collaboratively and independently. You excel at communicating with programmers and wet-lab scientists alike, and are able to explain complex concepts simply in written and spoken form. You are motivated to continually expand your skills and are keen to learn and apply new methods. You have a system for writing good code and managing your data, and see value in enabling reproducible research. You are organized, have strong time management skills and are capable of simultaneously working on multiple projects and meeting deadlines.

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/9711

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.

Located at the Harvard T.H. Chan School of Public Health, the Harvard Chan Bioinformatics Core (HBC) is a central resource for bioinformatics research, services and training at Harvard and across the Boston biomedical community. We work closely with biomedical scientists to develop and execute innovative workflows to analyze, interpret, visualize and distribute scientific discoveries derived from the analysis of high-throughput data.

Postdoctoral Positions

Postdoctoral Research Fellow in Climate Epidemiology

Description:

The Department of Biostatistics at Harvard TH Chan School of Public Health invites applications for a postdoctoral fellow position focused on the development of statistical methods for climate epidemiology. Working with an interdisciplinary team of statisticians, epidemiologists, environmental scientists, and clinicians, the fellow will develop practical statistical methods and apply them to real data with the goals of (1) quantifying the health impacts of historic extreme climate events such as tropical cyclones, flooding, and heat waves; (2) identifying key social, economic, environmental, and health modifiers of these effects; and (3) predicting the health burden of future extreme climate events. This research will improve our understanding of climate epidemiology, inform strategic preparedness efforts to minimize the adverse health impacts of future extreme climate events, and will generate novel and broadly applicable statistical and computational tools.

This position will involve the development and implementation of methods at the intersection of causal inference, spatio-temporal modeling, and machine learning in response to challenges presented by our rich integrated health and climate exposure datasets. These data include health outcomes from Medicare and Medicaid claims and birth cohorts and high-resolution multi-decade climate exposure metrics. In addition to methods development and data analysis, the fellow will be expected to write and publish peer-reviewed scientific papers, participate in group meetings and collaborative projects, and mentor more junior team members. The ideal candidate will have a strong statistical and computational background, experience processing and analyzing large datasets, and outstanding communication skills. The postdoctoral fellow will be supervised by Dr. Rachel Nethery at Harvard and will work closely with collaborators across multiple institutions.

Basic Qualifications:

• Doctoral degree in Biostatistics, Applied Statistics, Computer Science, or related field
• Experience developing and implementing statistical methods
• Experience analyzing real data
• Strong programming skills
• Excellent communication and writing skills
• Demonstrated ability to publish peer-reviewed scientific papers
• Commitment to collaborative work

Additional Qualifications:

• Experience implementing Bayesian models
• Experience working with spatial data
• Experience processing and analyzing large datasets
• Experience creating R packages and utilizing version control systems, e.g., Git/Github

Special Instructions:

• Cover letter
• Curriculum vitae
• One-page research statement and/or one representative 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/9845

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 Fellow

Description:
This is a two-year postdoctoral position developing statistical methods for finding patterns in complex biomedical data, working with Jeff Miller in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. The primary focus is on methods for noninvasive cancer detection using high-dimensional genomics data from blood samples (liquid biopsies). Models and methods of interest include hierarchical regression models, latent factorization models, nonparametric Bayesian models, models for sequential data, mixture models, machine learning algorithms, and robustness to model misspecification. This postdoctoral position will involve working with Dr. Miller and collaborators to develop statistical methods and software tools for accurate and noninvasive early cancer screening with liquid biopsies.

Qualifications:

Doctoral degree in Statistics, Biostatistics, Computer Science, Applied Math, or a related field. Advanced expertise in Bayesian statistics and machine learning is essential. Strong programming skills are required (e.g., in Julia, Python, R, C++). Experience with genomics data is a plus.

Primary author on at least one publication in a leading peer-reviewed journal.

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/9756

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 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.

Information on resources for career development and work/life balance at SPH can be found at: Career development and work/life balance.

For more information about the lab, please visit https://www.hsph.harvard.edu/onnela-lab/.

Postdoctoral Research Fellow / Research Associate Position in Data Science and Smartphone-Based Digital Phenotyping

Description:
The Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, 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 or Research Associate position to work on a digital women’s health study. For general details of the study, including study investigators, please see https://www.hsph.harvard.edu/news/press-releases/harvard-apple-nih-study/. The researcher will work as part of a large interdisciplinary team consisting of epidemiologists, clinicians, biostatisticians, computer scientists, and biomedical engineers to develop and apply methods to data collected by a smartphone or wearable device. This is a very exciting research area for anyone with a serious interest in temporally dense, high-dimensional data and its applications in women’s health. Methodologic challenges within this context will include dealing with missing data and dropout, the development of multi-stage sampling strategies for validation sub-studies, as well as the potential to develop novel data integration methods that link survey and passively collected data with other data types.

Qualifications:
Doctoral degree in biostatistics or statistics, computer science, applied mathematics, statistical physics, or a related quantitative field. Excellent programming skills in Python is essential, and familiarity with big data analysis frameworks, such as Apache Spark, is advantageous. Must be able to work independently and in a team environment and must have strong communication skills. Appointment at the research fellow or associate will depend on prior experience of the successful candidate.

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/9276

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.

For more information about the lab, please visit https://www.hsph.harvard.edu/onnela-lab/.

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 computational methods for analysis of high-throughput genetic and genomic data, including Whole Genome Sequencing association studies, integrative analysis of genetic and genomic data high-dimensional phenotype analysis, and genome-wide epigenetic association studies. 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.

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.

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/8842

Additional Information:
Administrative questions regarding this position can be sent to Susan Luvisi at biostat_postdoc@hsph.harvard.edu.
Scientific questions regarding this position can be sent to Xihong Lin
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