The Systems Modeling and Translational Biology (SMTB) department is seeking Quantitative Systems Pharmacology (QSP) Engineer at Principal Investigator level, passionate about using advanced mathematical modeling linked with artificial intelligence and machine learning approaches to inform key decisions in drug discovery and development.
The SMTB group uses mathematical modeling and simulation to address the relationships between drug properties, its disposition and the pharmacological and toxicological response to translate from in-vitro and in-vivo preclinical species to humans. This involves the integration and interpretation of data from many sources to help drive project decisions.
Working in a dynamic, multidisciplinary environment the successful candidate will develop, calibrate and use Quantitative Systems Pharmacology and Toxicology models of the relevant biological processes that link target modulation to clinical outcomes, link them to physiological based pharmacokinetics model (PBPK) for various delivery routes and leverage machine learning approaches (AI/ML) to provide actionable support for project decisions in drug discovery and development.
This position is an exciting opportunity to make a positive difference in the lives of patients by focusing the right targets, right drugs and the right patients.
Leverage existing and develop new PBPK models for various delivery routes and modalities and link them with the QSP and QST models to impact the progression of drug development programs
Develop Target Pharmacology Assessments to inform discovery programs
Further evolve modeling approaches for maximizing the positive impact on projects
Peer review modeling work from colleagues as appropriate, insuring high quality standards for the modeling work
Build, expand and refine the modeling capabilities and software platforms of SMTB
Exceptional collaborative behaviors to build and maintain relationships across R&D
Promote and increase the reputation of the modeling internally within R&D and externally including regulatory agencies
We are looking for professionals with these required skills to achieve our goals:
Advanced degree in chemical, mechanical or biomedical engineering, physics, applied mathematics, scientific computing or related field
Experience in building, validating and using complex, system level models of biological systems
Core competency in numerical analysis focusing on differential equations and parameter optimization.
Working knowledge of statistical and machine knowledge approaches
Expertise in scientific computing and programming - in particular Matlab and Simbiology
Experience building and using Quantitative Systems Pharmacology/Toxicology (QSP/QST), PBPK or mechanistic PK/PD models
Experience in influencing decisions and experimental design by using appropriate modeling approaches that integrate all available data
Experience in linking QSP/QST models with mechanistic drug pharmacokinetics
Experience working in cross functional / multi-disciplinary teams
If you have the following characteristics, it would be a plus:
Experience in linking QSP/T modeling with ‘omics data and big data analytics and machine learning
Experience building and using QSP/QST, PBPK and mechanistic PK/PD models
Knowledge and understanding of drug delivery, absorption, distribution and metabolism, pharmacology and toxicology, PK/PD modeling, physiological based modeling and translational sciences
Familiarity with the challenges of drug discovery and forward thinking with respect to the general application of mathematical models in discovery and development
Scientific curiosity and demonstrated ability to learn
Excellent written and oral communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial, collaborative attitude
- Numerical Analysis
- Mathematical Modeling
- Differential Equations
- Drug Discovery