Postdoctoral Fellow, Materials Science & Characterization PPG Analytical
About The Position
AbbVie is seeking a scientifically rigorous and collaborative postdoctoral fellow to join the Materials Science & Characterization Team in PDST / PPG Analytical. The fellow will advance AI-enabled, multimodal characterization of targeted lipid nanoparticles (tLNPs) by generating, integrating, and curating analytical datasets using internally generated data and data received primarily from external laboratories and contract research organizations (CROs). During the first half of the appointment, the role will focus primarily on analytical data generation, 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 fellow will design and execute experiments, analyze and interpret data, collaborate with multidisciplinary partners, and contribute to scientific publications, presentations, and internal knowledge sharing. The position aims to generate insights, methods, or capabilities that advance Product Development Science & Technology priorities and support future commercial delivery approaches for Oncology and Immunology Therapies.
Requirements
- PhD in materials science, chemical engineering, biophysics, physical chemistry, analytical chemistry, or a related discipline.
- Experience with tLNP formulation, manufacturing, characterization, and stress, stability, and comparative studies, or a combination of these areas.
- 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 interest in developing interpretable machine-learning approaches.
- Ability to integrate complex formulation, process, stability, biophysical, bioanalytical, and functional datasets from 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 and ability to present complex scientific concepts clearly.
- 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.
- Strong organizational skills and ability to manage multiple priorities and timelines.
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 experiments and analytical workflows; define data-transfer, quality-assessment, and documentation requirements for datasets generated internally and externally; and establish consistent reporting standards.
- Define and manage batch metadata, sampling strategies, sample-tracking requirements, and provenance for samples and data generated internally or received from external partners.
- Build, integrate, curate, and maintain validated multimodal characterization datasets across analytical platforms, initially emphasizing data generation, quality assessment, harmonization, and curation.
- Analyze and harmonize quantitative features related to particle size, morphology, aggregation, thermal behavior, spectroscopy, encapsulation, and accessibility.
- 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 as the dataset becomes sufficiently curated.
- Troubleshoot technical challenges; assess the quality, completeness, and interpretability of analytical data; recommend alternative approaches; and design targeted follow-up experiments or analyses.
- Communicate scientific plans, progress, results, and recommendations across multidisciplinary teams.
- 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.
- Conduct work in alignment with applicable safety, quality, compliance, and documentation expectations.
Benefits
- Paid time off, including vacation, holidays, and sick time.
- Medical, dental, and vision insurance for eligible employees.
- 401(k) for eligible employees.
- Eligibility to participate in short-term incentive programs.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
