Postdoctoral Fellow, Materials Science & Characterization PPG Analytical

Job Description

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

• Own and execute fit-for-purpose tLNP analytical-data programs, incorporating internally generated data and analytical data received predominantly from external laboratories and CROs, in alignment with project objectives.
• Design rigorous experiments and analytical workflows; define data-transfer, quality-assessment, and documentation requirements for internally and externally generated datasets; and establish consistent reporting standards to support high-quality tLNP development and decision-making.
• Define and manage batch metadata, sampling strategies, sample-tracking requirements, and provenance for samples and data generated internally or received from external partners
• Build, integratecurate, and maintain validated multimodal characterization datasets across analytical platforms, with an initial emphasis on data generation, quality assessment, harmonization, and curation.
• Analyze and harmonize quantitative features related to particle size, morphology, aggregation, thermal behavior, spectroscopy, encapsulation, and accessibility.
• Following establishment of a sufficiently curated analytical dataset, develop and apply interpretable statistical, computational, and machine-learning approaches to identify quality-relevant attributes, fingerprint batches, assess heterogeneity and stability risks, and evaluate structure–function relationships.
• Troubleshoot technical challenges; assess the quality, completeness, and interpretability of internally and externally generated analytical data; recommend alternative approaches; and design targeted follow-up experiments or analyses.
• Collaborate across multidisciplinary teams by communicating scientific plans, progress, results, and recommendations;
• Contribute to publications, abstracts, presentations, patent filings, and other scientific communications;
• Maintain awareness of relevant scientific literature, emerging technologies, and external developments in the field.
• Ensure work is conducted in alignment with applicable safety, quality, compliance, and documentation expectations.

Qualifications

• PhD in materials science, chemical engineering, biophysics, physical chemistry, analytical chemistry or related discipline.
• Experience with tLNP formulation, manufacturing, characterization, and stress, stability, and comparative studies or a combination thereof.
• Expertise in advanced analytical instrumentation and nanoparticle characterization, including Cryo-EM, SAXS, DLS, NTA, DSC, fluorescence, and or optical techniques.
• Strong combination of hands-on experimental expertise, analytical data generation and curation experience, quantitative data analysis, multivariate statistics, and an interest in developing interpretable machine-learning approaches.
• Ability to integrate complex formulation, process, stability, biophysical, bioanalytical, and functional datasetsfrom internal and external sources; apply sound scientific judgment; and communicate insights clearly.
• Record of scientific productivity, including publications, presentations, or demonstrated research accomplishments.
• Excellent problem-solving skills, including critical thinking, data interpretation, and scientific judgment.
• Strong written and verbal communication skills with the ability to present complex scientific concepts clearly.
• Demonstrated ability to work independently while seeking input and feedback from peers and supervisors.
• Ability to collaborate effectively in a multidisciplinary, diverse, and fast-paced environment.

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