Drive development and application of novel QSP Immunology models to facilitate back-translation efforts from internal late development programs.
Function as part of team to develop and rigorously assess the functionality, feasibility, efficiency and validity of new models.
Conduct extensive literature review to identify suitable mechanistic elements, interactions, rate constants and sub-modules for incorporation into larger systems (pharmacodynamic and efficacy) models.
Leverage algorithms for parameter optimization and sensitivity analysis.
Analyze model predictions and identify suitable strategies to resolve issues (whenever occur) pertaining to model performance and inaccuracy.
Network with experts in Translational Medicine, Discovery Biology, Clinical Development, and other groups to share learning's that maximize the translational value of QSP modeling.
Participate in internal scientific (QSP-related) discussions.
Build and maintain QSP expertise through a personal track record of publication, attendance/participation in conferences, scientific workshops, etc.
Ph.D. in Engineering, Mathematics, Bioinformatics, Pharmacometrics, Systems Biology/Pharmacology or a related field with a proven record of productivity as demonstrated by publications and conference presentations.
Demonstrated experience in Computational Systems Biology field.
Excellent understanding of theory, principles and statistical aspects of advanced mathematical modeling and simulation, including numerical methods, parameter estimation/optimization, and ordinary differential equations (ODEs).
Experience in building, validating and using QSP models or deterministic models of biological pathways/systems to support basic science and/or translational clinical research would be particularly desirable.
Excellent oral and written communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial and collaborative attitude.
Hands-on experience with modeling software like MATLAB is required.
Experience with general programming and data analysis tools/languages such as Python, R is desirable.
Ability to learn new areas of biological sciences and build on solid foundation of quantitative skills to develop mechanistic disease/pharmacology models.
Good understanding of the basic principles of pharmacokinetics and pharmacodynamics.
Previous (academic or industry) experience in QSP or a closely related field are highly desired