Role Summary
Postdoctoral Research Fellow in Analytical Development focused on developing next-generation spectromicroscopy strategies (Raman, FTIR, OPTIR, MALDI MS) to characterize long-acting injectable (LAI) drug depots and understand their in-tissue chemical and solid-state behavior. This role provides a unique opportunity to integrate advanced spectroscopy, multimodal imaging, chemometrics, and machine learning to address key scientific questions related to drug release, depot stability, and injection-site responses. Collaborates closely with Formulation, DMPK, DPP, and Toxicology teams and presents research in internal forums and external publications and conferences.
Responsibilities
- Develop and optimize Raman, FTIR, OPTIR, and MALDI MS methods to characterize depot chemistry and solid-state forms across synthetic media and ex vivo tissues.
- Perform stress-induced solid form transition studies (pH, oxidative stress, moisture-driven transformations) for model LAI systems.
- Build machine-learning and chemometric models (CLS, PLS, PCA, image-based segmentation) to map drug distribution, degradation, and polymorphic transitions.
- Characterize drug depots in porcine dermis, fat, and muscle tissue to assess in-tissue transformations and correlate analytical findings with histopathology and ISR outcomes.
- Integrate multimodal datasets (Raman/IR/MALDI) into unified chemical imaging workflows to support formulation development.
- Collaborate closely with Formulation, DMPK, DPP, and Toxicology teams on cross-functional LAI development.
- Present research in internal forums and contribute to high-impact external publications and conferences.
Qualifications
- Required: PhD in Chemistry, Chemical Engineering, Pharmaceutical Sciences, Materials Science, Optics Science, Biomedical Engineering, Mechanical Engineering or related discipline.
- Required: Hands-on experience with Raman and/or FTIR spectroscopy.
- Required: Proficiency in Python and/or MATLAB for spectroscopic data processing, multivariate analysis, and deep learning for image segmentation.
- Required: Strong scientific curiosity, problem-solving ability, and independent research skills.
- Required: Demonstrated track record of conference presentations or peer-reviewed publications.
- Preferred: Experience with OPTIR, digital microscopy, fluorescence microscopy, quantitative phase microscopy, SEM/EDS, XRPD, MALDI-IMS, HPLC, LCMS.
- Preferred: Experience with building and maintaining advanced or non-linear spectroscopy/microscopy instruments.
- Preferred: Background in solid-state characterization, LAI formulation, or tissue-based imaging workflows.
- Preferred: Familiarity with chemometric platforms (e.g., PLS Toolbox).
- Preferred: Excellent communication skills and ability to work in a matrixed, multidisciplinary environment.
Skills
- Multimodal imaging and data integration
- Chemometrics and multivariate analysis
- Machine learning and deep learning for image analysis
- Scientific communication and cross-functional collaboration
- Experimental design and problem solving