Drs. Jacqueline Starr (Brigham and Women's Hospital) and Kyu Ha Lee (Harvard T. H. Chan School of Public Health) invite applicants to join their joint laboratory group. Multiple positions range from pure biostatistical methods development to more applied methods evaluation and data analysis. This is an opportunity to work in an interdisciplinary group that values rigor and includes biostatisticians, epidemiologists, microbiologists, and imaging experts. We seek to bridge principled causal inference with high-dimensional data analysis.
NIH funding supports our work to develop novel multivariate spatial data (or other count data) models for application to microbiome data. The most immediate need is for a fellow to help develop new multivariate spatial statistical models in a Bayesian framework. Other possible projects include developing highly multivariate Bayesian models for analyzing sequence count data or for integrated 'omics applications involving microbiome data.
Also needed is a fellow to conduct applied methods development, e.g., comparing performance of our new methods to other approaches in simulated and real datasets. The fellow would be encouraged to identify other new applied datasets and projects.
Fellows are jointly supervised by Drs. Lee (HSPH, Nutrition and Biostatistics), Starr (BWH and Harvard Medical School), and Brent Coull (HSPH, Biostatistics). Mentors meet with fellows at least weekly and provide timely and constructive feedback to support lab members' career development goals. Fellows also participate in meetings and working groups with a much broader community of investigators.
Responsibilities will depend on the applicant's fit to each position and might include:
1) Develop Bayesian spatial statistical models for analysis of microscopic image data (especially fluorescence in situ hybridization images of microbial biofilm). This includes creating software packages to disseminate newly developed methods.
2) Apply Bayesian multivariate spatial or count data models to a) evaluate performance under different real and simulated data settings, b) compare performance to that of other approaches, and c) perform variable selection or hypothesis testing to answer applied scientific questions.
3) Help the laboratory identify existing microbiome or other image data, help process images (e.g. image segmentation), and perform spatial statistical analysis to investigate methodologic performance and address applied scientific questions.
4) All fellows will collaborate on other methods development projects in the laboratory. The common theme is high-dimensional data analysis for variable selection and causal inference, with special emphasis on microbiome-related data and other 'omics-microbiome integration.
• Required: Doctoral degree in biostatistics, statistics, computational biology, or related quantitative fields (e.g., epidemiology for the applied fellow).
• Required: Excellent programing skills in R, C/C++ and/or Fortran
• Required: Strong organizational, communication, and writing skills.
• Preferred: Strong skills in Bayesian modeling and Markov chain Monte Carlo methods.
• Preferred: Experience with multivariate analysis, variable selection methods, spatial analysis, or image analysis.
BWH is an Affirmative Action Employer. By embracing diverse skills, perspectives and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
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