2019 Experiential Internship (Graduate Level) - Process Analytical Chemistry
2301 N 72nd Ct
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The AbbVie Experiential Internship Program
As an AbbVie Experiential Intern, you'll participate in a paid, ten to twelve-week summer program that is focused on providing students with robust projects throughout the summer. As an intern, you will be located at our corporate headquarters in north suburban Chicago, with housing and shuttle services for eligible students.
Department Overview - Process Analytical Chemistry
The process analytical chemistry group develops and executes methods for analytical characterization of pharmaceutical ingredients and related molecules important to development of a safe, robust, and cost effective synthetic route for drug substance.
The intern will drive the use of high-throughput UHPLC/HPLC/SFC screening and chemometrics modeling to enable a rapid pace of method development. A core part of the internship will be focused on fundamental characterization of stationary phase interactions under different chromatographic conditions using selected structural analogs and homologs in addition to, where applicable, pipeline assets. The trove of data will be used for developing quantitative structure analytical relationship models for predicting retention behavior of molecules. Judicious use of established chromatographic modeling platforms, statistical packages, and machine learning will be incorporated. The overarching goal of the internship is to identify well defined and shortest paths, in time and resource efficient manner, for an initial method as well as a robust method.
At least 1 year completed of graduate level studies prior to internship experience
PhD candidate in Analytical Chemistry, Chemical Engineering, Pharmaceutical Sciences, or related discipline
Must be enrolled in school the semester following your internship
Minimum Cumulative GPA: 3.0/4.0
Must be authorized to work in the U.S. on a permanent basis without requiring sponsorship (students on an F1 visa with CPT can be accommodated).
Skills and knowledge: Computational Chemistry (COSMO and Materials Studio), Machine Learning (Regression, Classification, Clustering), Statistical modeling, and experience with Linux environment;
Experience in chromatography method development, proficiency with HPLC/UHPLC required
Strong background in separation science, experience in multiple modes of chromatography, and experience in chiral method development highly desired
Experience in high-throughput screening, high-efficiency/peak capacity separations, and/or ultrafast analysis is preferred but not necessary
Familiarity in modeling of chromatography data (using DryLab, Fusion, ChromSword etc), statistical analysis, and/or machine learning is desired but not necessary
Excellent communication skills and demonstrated ability to work independently;
Thrive in a team environment and highly innovative.