Office of Postdoctoral Affairs

Position Detail

Postdoctoral fellowship to develop statistical methods and statistical machine learning algorithms to construct a system for population pharmacokinetic (PK) and pharmacodynamic (PD) studies using electronic health records (EHRs) data.

Department	Biostatistics
Full Description	This postdoctoral fellowship is to develop and apply statistical machine learning algorithms to identify correct medication dosing data extracted from electronic health records (EHRs), and to perform population PK/PD studies using the datasets constructed from the EHRs. This is a 1 year post-doc (with the possibility of extension) funded to work on a project titled, “System construction for population pharmacokinetic and pharmacodynamic modeling using electronic health records: toward precision medicine,” funded by the National Institute of Health. Duties include working with a team led by study project principal investigator, Leena Choi (Vanderbilt), to develop and apply statistical machine learning algorithms to identify correct medication dosing data, and to perform population PK/PD studies using the datasets constructed from the EHRs. A PhD. in biostatistics, statistics, or a related field is required. Experience with statistical machine learning is desired. Experience with population PK/PD modeling is desirable, but not required. Strong statistical knowledge and computation skills are required.
Application Details	Please send a cover letter, CV, and contact information for 3 references to Leena Choi. Contact: Leena Choi, PhD Phone: 615-343-3497 Application Deadline: Consideration of applications will begin immediately, and will continue until the position is filled. Interviews will be conducted during the Annual Joint Statistical Meetings in Vancouver, British Columbia, from July 30- August 2, 2018. This position is available starting in September 2018 and appointment will be made for a one-year contract.
Contact:	Leena Choi, PhD : leena.choi@vumc.org : (615) 290-2560
Posted	2018-07-26 13:08:55

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