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 Analyst and/or Trainer

Description:

We seek a talented and highly motivated Bioinformatician to join our dynamic, collaborative team focused on high-throughput sequencing (HTS) applications. The ideal candidate will have experience analyzing and interpreting omics data, and is enthusiastic to share their expertise through the core’s consulting and education programs. The candidate will work as part of a team of bioinformaticians to develop and apply bioinformatics workflows to support both standard and custom data analysis for variant calling, transcriptomics, epigenomics and multi-omics projects. This role provides a unique and rewarding opportunity to support and train world-class researchers and to make a profound impact on human health.
About us
The Harvard Chan Bioinformatics Core (HBC) is a center for bioinformatics research, services and training at the Harvard T.H. Chan School of Public Health. We work closely with biomedical scientists across Harvard to implement approaches for analysis, interpretation, visualization, and dissemination of scientific discoveries using high-throughput omics data.
Our multidisciplinary team works with biomedical research groups in the Harvard, MIT and Broad communities, and at local hospitals and industry. Our core has a strong educational mission. Members of the core participate in training activities, with the goal to enable experimentalists to analyze their data independently. The HBC emphasizes teamwork and a supportive environment where we learn from each other.
About you
You have a background in biomedical or quantitative science and a strong interest in working with biomedical researchers. You thrive on scientific challenges, and enjoy collaborating with an interdisciplinary team. You can combine your knowledge of biology and computation to communicate effectively with programmers and wet-lab scientists alike. You are an independent learner, are keen to explore and apply new methods, and are motivated to continually expand your skills. You follow best practices for code and data management, have good organization skills, and are capable of simultaneously working on different projects and deadlines. You are experienced with high-throughput sequencing analysis and are comfortable mentoring junior researchers. An interest and/or experience in teaching all of the above-mentioned skills to wet-lab biologists, is a plus.
Duties
The Bioinformatician will support selected research projects at the Harvard Chan School, Harvard and the broader Boston biomedical community. They will provide expertise in the use of specialized bioinformatics tools and analysis methods. They will collaborate with researchers to design, analyze and interpret experiments.
Coordinating with experienced Core staff, the candidate will analyze incoming data using existing analytical approaches in the Core, and assess and/or develop new methods where appropriate. They will document their work thoroughly, adhere to NIH requirements for data management, and provide manuscript-level reporting of final analyses and results. They will summarize, analyze, and visualize data using advanced techniques, and present it clearly to collaborators. If interested the candidate will have opportunities to with the training team to teach workshops and develop new training content geared towards Harvard graduate students, postdocs, research staff and faculty.
Qualifications:
●        Ph.D. in Bioinformatics, Computational Biology, Genomics, Biostatistics or Biological Sciences with working knowledge of molecular biology.
●        At least 2 years of postdoctoral experience in academia or industry using a broad range of current bioinformatics approaches for common applications. Expertise in at least one of the following HTS applications is required:
          o   Whole genome or exome-sequencing
          o   Transcriptomics (bulk, small or single cell RNA-seq)
          o   Epigenomics (ChIP-seq or ATAC-seq)
●        Demonstrable ability to apply statistical approaches to analyze data, interpret and present results.
●        Excellent analytical and programming skills.
●        Excellent communication and time management skills.
Additional Qualifications:
●        Knowledge of and experience with cancer datasets is a plus.
●        Ability to produce reproducible code (R Markdown, Latex, Jupyter notebooks, etc).
●        GitHub repositories and publications that can showcase your skills.
●        A strong interest in sharing expertise and/or teaching.
Special Instructions:
Please also include:
– 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.

Postdoctoral Research Fellow in Biostatistics

Description:

This is a 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.  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 analyzing high-dimensional biomedical data from cancer genomics and clinical applications.
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.
Special Instructions:
Please also include:
– 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/.

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.

For the Research Associate position two years of postdoctoral experience is preferred.

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.