Postdoc in Statistical Science at Duke

Duke University Department of Statistical Science has multiple Postdoctoral Associate positions to work with Professors David Dunson and Amy Herring in developing novel statistical methods for high-dimensional, complex, and/or structured data. A particular focus is on inferring latent structure (e.g., clusters) in data, while characterizing uncertainty. Motivating applications include ecology studies of biodiversity, environmental health, infectious disease subtyping, and neuroscience. The research aims to develop fundamentally new classes of statistical models, along with theory support and efficient computational algorithms, motivated by scientific and medical applications. The work should lead to publications in leading statistical journals, as well as practical algorithms and code for use by scientists in other fields. Ideal candidates will be finishing a PhD in statistics, mathematics, or a closely related field, with rigorous technical training, a strong publication record, and proven abilities in applied-driven methods development. Interest and experience in analyzing large audio and image datasets, complex Bayesian modeling, and collaboration with researchers outside statistics are pluses. The positions can start anytime, with application review beginning immediately. Teaching opportunities are available to interested and qualified candidates. Please submit a CV, a brief letter of interest, and the names of 3 or more references to this link . Duke University is an Affirmative Action/Equal Opportunity Employer committed to providing employment opportunities without regard to age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status. Our community values collaboration, innovation, creativity, and belonging. We believe that a diverse range of perspectives, backgrounds, and experiences enriches our collective success. We are committed to creating a secure and welcoming environment where all members feel respected, heard, and valued. #J-18808-Ljbffr The International Society for Bayesian Analysis