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Position: Postdoc Scholar
Institution: Pennsylvania State University
Location: University Park, Pennsylvania, United States
Duties: The Department of Statistics at The Pennsylvania State University invites applications for a Postdoctoral Scholar position to work on collaborative projects under the supervision of Dr. Francesca Chiaromonte and Dr. Kateryna D. Makova. Potential research projects include (but are not limited to) integrating and analyzing large scale ‘omics’ data to study G-quadruplex motifs and their role in genome evolution, and developing novel statistical and computational methods for analyzing large scale ‘omics’ data
Requirements: A Ph.D. in Statistics, Biostatistics, Bioinformatics, Computational Biology or directly related field at the time of appointment is required. The successful applicant is expected to have a solid background in statistical methods and models, strong computational skills, research experience showing independence and innovation, and demonstrated academic writing skills. Prior experience in statistical analysis of large scale ‘omics’ data is strongly preferred
   
Text: Postdoc Scholar The Department of Statistics at The Pennsylvania State University invites applications for a Postdoctoral Scholar position to work on collaborative projects under the supervision of Dr. Francesca Chiaromonte and Dr. Kateryna D. Makova. Potential research projects include (but are not limited to) integrating and analyzing large scale ‘omics’ data to study G-quadruplex motifs and their role in genome evolution, and developing novel statistical and computational methods for analyzing large scale ‘omics’ data A Ph.D. in Statistics, Biostatistics, Bioinformatics, Computational Biology or directly related field at the time of appointment is required. The successful applicant is expected to have a solid background in statistical methods and models, strong computational skills, research experience showing independence and innovation, and demonstrated academic writing skills. Prior experience in statistical analysis of large scale ‘omics’ data is strongly preferred
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