Postdoctoral Fellow Purification Development

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

Purpose

We are seeking a highly motivated postdoctoral researcher to join our multidisciplinary team focused on developing a QSAR based modeling workflow to transform purification process development for complex biomolecules in our pipeline. This position offers an exciting opportunity to integrate chromatography, machine learning tools and analytical characterization to address one of the key challenges in biologics manufacturing - efficient process development of increasingly complex and diverse molecular constructs. The successful candidate will establish a robust dataset by performing chromatographic experiments on various industrially relevant molecules, use molecular modeling tools to generate descriptors and use machine learning tools to develop predictive models for these complex biomolecules and their impurities. In parallel, the researcher will collaborate closely with the protein engineering and analytical team to establish LC/MS workflows to gain molecular level insights into these complex molecules that will feed into the modeling workflow. The goal of this position is to generate a modeling framework and insight to inform molecular design strategies to help with developing facile purification processes for complex biomolecules. The candidate will also drive publications and presentations, externally and/or internally, related to the outcomes of this work

Responsibilities

• Design and conduct chromatography experiments to generate high quality data sets for machine learning model development.
• Apply machine learning and modeling techniques to extract insights to identify key process parameters and guide rational design of chromatographic steps.
• Collaborate with multidisciplinary team