Quantitative Systems Pharmacology (QSP) is a discipline that uses mechanistic mathematical models and disease platforms to enhance the robustness and quality of decision-making from exploratory research through clinical development. The QSP group at Pfizer is seeking a highly motivated postdoctoral candidate to develop and analyze mechanistic mathematical models of LNP-mRNA based therapies to allow assessment and prediction of key mechanisms driving clinical variability in efficacy and safety for optimal dosing decisions. This Postdoctoral Fellowship is an opportunity to work within a dynamic group who are at the forefront of the application of mechanistic systems models to address critical uncertainties in drug discovery and development.
The successful candidate will have earned a Ph.D. in Applied Mathematics, Engineering, Physics, Pharmaceutical Sciences, or other related discipline and has a demonstrated track record in scientific publication. The postdoctoral fellow will develop and analyze mathematical models of LNP-mRNA based therapies that integrates biological knowledge and available data with the goal of providing tools for pre-clinical to clinical translation, dose selection, and clinical trial design. They will work collaboratively with biologists, clinicians, clinical pharmacologists, pharmacometricians, and QSP and nonclinical modelers to improve designs for effective and durable LNP-mRNA based therapies in multiple therapeutic areas. ROLE RESPONSIBILITIES
The Postdoctoral Fellow will develop and analyze mathematical models toward enhanced quantitative understanding of the effectiveness and safety of LNP-mRNA based therapies utilizing data available in the literature or internal data from pre-clinical/clinical programs. This may include but is not limited to:
- Employing modeling and simulation techniques for predicting LNP-mRNA based therapy outcomes
- Identifying relevant data (in vitro and in vivo preclinical and clinical study data) for model development, optimization, and validation
- Designing nonclinical experiments aimed towards generating data for model validation and testing of relevant hypotheses
- Effectively communicating model results and outcomes to scientists in both quantitative and non-quantitative disciplines
- Primary authorship on scientific publications and presenting at internal and external scientific meetings.
- Recent Ph.D. (0-3 years) in Applied Mathematics, Mathematical Biology, Chemical Engineering, Biomedical Engineering, Physics, or related discipline with strong numerical components focusing on mathematical modeling and simulation.
- Training or previous experience in building QSP or differential equation based models of biological or physiological pathways/systems is required.
- Understanding of theory, principles, and statistical aspects of mathematical modeling and simulation, including parameter estimation techniques
- In-depth understanding of ordinary differential equations (ODEs) and how these can be applied in the development of complex models of biological pathways and systems
- In-depth, hands-on knowledge of modeling and simulation software (MATLAB, C/C++ preferred)
- Keen interest in learning new areas of biology and building on a solid foundation of quantitative and computational skills
- Self-directed with ability to work independently
- Team player
- Excellent communication and writing skills
- Primary authorship on relevant publications in peer-reviewed scientific journals
Ability to perform mathematical calculations and ability to perform complex data analysisNON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS
Office-based position with infrequent travel to scientific conferences and/or business meetings.OTHER JOB DETAILS
Eligible for Relocation Assistance
Must Have Work Authorization in US
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Research and Development