Allergan
Allergan
Posted 3 days ago
Full Time
North Chicago, Illinois
In Person
Smart Summary
Responsibilities
The postdoctoral fellow will lead the generation, integration, and curation of analytical datasets for targeted lipid nanoparticles using internal and external data. They will also apply statistical and machine-learning techniques to identify quality-relevant attributes and structure-function relationships.
Qualifications
You have a PhD in materials science, chemical engineering, biophysics, physical chemistry, or a related discipline, with expertise in advanced analytical instrumentation and nanoparticle characterization. You are skilled in experimental design, data generation and curation, quantitative analysis, and have a strong record of scientific productivity and collaboration.
Must Have Skills for ATS
Cryo-EM
SAXS
DLS
NTA
DSC
fluorescence
optical techniques
multivariate statistics
machine-learning
Job Description
About AbbVie
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 including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.
Purpose:
AbbVie is seeking a highly motivated, scientifically rigorous and collaborative postdoctoral fellow to join the Materials Science & Characterization Team in PDST / PPG Analytical to advance AI-enabled, multimodal characterization of targeted lipid nanoparticles (tLNPs). This position offers an opportunity to lead the generation, integration, and curation of an analytical dataset using experimental characterization data generated internally and data received predominantly from external laboratories and contract research organizations (CROs). During the first half of the appointment, the primary focus will be on analytical data generation, data quality assessment, standardization, and curation; during the second half, the fellow will increasingly apply feature extraction and interpretable statistical and machine-learning analyses to identify attributes associated with particle heterogeneity, stability, product quality, and functional performance in support of tLNP development. The successful candidate will design and execute experiments, analyze and interpret data, collaborate with multidisciplinary partners, and contribute to scientific publications, presentations, and internal knowledge sharing. The goal of this position is to generate new insights, methods, or capabilities that advance Product Development Science & Technology priorities and support innovation of future commercial delivery approaches for Oncology and Immunology Therapies.
Responsibilities
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.
AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.
US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html
US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
https://www.abbvie.com/join-us/reasonable-accommodations.html
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