Opportunities

POSTDOCTORAL RESEARCH POSITION: DATA SCIENCE
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
Qualifications:
  • Doctoral degree in Computer Science, Statistics, Biostatistics, or related field.
  • Experience in analyzing real data, strong programming skills, and familiarity with statistical methods is preferred.
  • Excellent communication and writing skills desired.
  • The ideal candidate is an independent, solution-oriented thinker with a strong background processing very large data sets, applying analytical rigor and statistical methods, and driving toward actionable insights and novel solutions.
  •  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.

CLICK HERE TO LEARN MORE AND APPLY

POSTDOCTORAL RESEARCH POSITION: BIOSTATISTICS
The Department of Biostatistics at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Fellow position for 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.   The position will be under the joint supervision of Dr. Francesca Dominici in the Department of Biostatistics and Dr. Rachel Nethery, and in collaboration with Dr. Danielle Braun. Applicants should have an interest in developing and applying novel and state-of-the-art statistical and data science methods in environmental health.
Qualifications:
  • Doctoral degree in Computer Science, Statistics, Biostatistics, or related field.
  • Experience in analyzing real data, strong programming skills, and familiarity with statistical methods is preferred.
  • Excellent communication and writing skills desired.
  • The ideal candidate is an independent, solution-oriented thinker with a strong background processing very large data sets, applying analytical rigor and statistical methods, and driving toward actionable

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
CLICK HERE TO LEARN MORE AND APPLY

 

RESEARCH ASSISTANT II

The Department of Biostatistics is a very busy and diverse department focused on research and teaching. Our 70+ faculty are leaders in the development of statistical methods for clinical trials and observational studies, studies on the environment, computational biology, and genomics/genetics. We offer degree programs in biostatistics, computational biology and data science; currently, more than 180 students are enrolled in these programs.

Our team invites applications for a Research Assistant (RA) in the National Studies on Air Pollution and Health (NSAPH) group. The work will involve close collaboration with Dr. Dominici, co-Director of the Harvard Data Science Initiative and Clarence James Gamble Professor of Biostatistics, Population and Data Science (https://www.hsph.harvard.edu/francesca-dominici/), and her team of PhD students, postdoctoral fellows and data scientists. Senior research scientist, Dr. Danielle Braun, who is a senior member of the Dominici Lab, will supervise the position. The team’s current research focus is on the development and implementation of statistical and machine learning methods to estimate causal effects of environmental exposures on health in the presence of high dimensional data, multiple exposures, strong confounding, and complex, non-linear relationships. The team has access to many rich datasets in these areas, including high-resolution air pollution data and health data from Medicare, Medicaid, and private insurance companies, to which these methods will be applied in order to answer high impact scientific questions.

Duties & Responsibilities:

  • Assist lab members in conducting specific research projects including but not limited to conducting literature review, statistical analysis, and revising and editing manuscripts to respond to reviewer comments.
  • Develop early-stage research projects and assist in developing projects in collaboration with potential students and other collaborators.
  • Explore funding ideas and identify specific research gaps that funding proposals could address.
  • Lead individual and collaborative data analysis projects and conduct preliminary data analysis for funding proposals
  • Develop statistical methods and software.
  • Serve as principal liaison to disseminate the group’s findings on specific research projects.
  • Organize and schedule research meetings.
  • Manage scientific research activities, including detailed to do lists, action items between the lab and funded collaborations with other labs (internal to Harvard and external)
  • Perform other duties as assigned.

Basic Qualifications:

  • 2+ years of related work experience required; a combination of education and experience may be considered
  • Experience with statistical programming software such as R and/or Python, or related required.

CLICK HERE TO LEARN MORE & APPLY.

Please contact us on nsaph.harvard@gmail.com for more information.