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These positions come at a time of dynamic leadership and with significant investment in the College of Science, which comprises the computing, physical, and life sciences at Purdue. The College has committed significant investment in Data Science and provides highly competitive start-up packages in Statistics. The Department’s teaching and research activities cover a broad range of topics integral to modern statistics (theoretical, methodological, computational, and applied), probability, and data science, as well as connections to computational finance and actuarial science, machine learning and artificial intelligence, spatial statistics and environmental statistics, and statistical genetics and genomics. For more information, see http://www.stat.purdue.edu/.
The postdoctoral fellow will be mentored by Dr. Jordan Awan (https://www.stat.purdue.edu/people/faculty/jawan.html ) and will have an opportunity to work on exciting projects at the intersection of statistics and differential privacy. Projects may develop new privacy techniques in methodology and computation (e.g., simulation-based inference, bootstrap theory for data privacy, Bayesian computing), areas of application (e.g., network data, functional data, causal inference), or foundations of differential privacy.
The initial appointment is for one year, renewable for one additional year subject to satisfactory performance. The postdoctoral fellow will be mentored in all aspects of manuscript preparation and grant proposal writing. Substantial opportunities for collaboration exist at Purdue and beyond. Support is available for dissemination of research in national and international conferences. Teaching is not required, but can be arranged if desired. The start date is negotiable, but could be as early as Summer 2024, and search will continue until the position is filled.
We seek exceptionally strong candidates with a PhD in statistics, biostatistics, computer science, applied mathematics or other related fields. Candidates with demonstrated accomplishment in academic research, as can be judged from published papers or publicly available (e.g., arXived) preprints, will be given priority. Strong programming skills are required, with preference for R or Python. Prior experience and demonstrated interest in differential privacy is a plus.