Investigator Quantitative Systems Pharmacology, Open to flexibility
Investigator Quantitative Systems Pharmacology, Open to flexibility
The Systems Modeling and Translational Biology (SMTB) department is seeking a Quantitative Systems Pharmacology engineer/scientists passionate about using advanced mathematical modeling and computational sciences to develop mechanistic models of disease (QSP) and mechanistic models of toxicology (QST).
The SMTB group uses mathematical modeling and simulation to address the relationships between drug properties, its disposition and the pharmacological and toxicological response translating between in-vitro, in-vivo preclinical species and human. This involves the integration and interpretation of data from many sources to help drive project decisions.
This Quantitative Systems Pharmacology role involves developing QSP/QST models and using them to influence critical decisions in the drug discovery and development from target selection to the identification of the best modality, delivery route to engage the target, optimal dose selection including inputs into clinical trial design.
Working in a dynamic, multidisciplinary environment the successful candidate will develop, calibrate and use complex Quantitative Systems Pharmacology/Toxicology models linked with Physiological Based Pharmacokinetic Models for linking drug disposition and target biology with clinical outcomes for various diseases.
This position is an exciting opportunity to make a difference in the lives of patients by applying mechanism based mathematical modeling coupled with computational science and biological knowledge to drug discovery and development.
- Responsible for providing appropriate modeling support to assigned Discovery Performance Units (DPU)/Therapeutic Areas (TA)/Platform Technology and Science(PTS) line and peer review modeling work from colleagues as appropriate, insuring high quality standards for the modeling work
- Further develop unique expert modeling skills. Provide expert modeling consultancy, training and support to others in your unique areas of expertise
- Develop and execute a modeling strategy for each DPU/TA/PTS line assigned insuring availability of appropriate modeling capability
- Build, expand and refine the modeling capabilities/platforms of SMTB
- Promote and increase the reputation of the modeling both internally within R&D and externally including regulatory agencies
- Aid the interpretation of the data we generate and integrate it with that of clinical and pre-clinical partners to help project decisions
- Use modeling as a thinking tool to help generate better hypothesis and design better, model informed experiments
- Provide consultancy and training resource to increase the overall kinetic thinking and modeling capability of staff both within the department and outside in PTS and R&D
- Exceptional collaborative behaviours to build and maintain relationships with both R&D customers as well as the wider modeling community in GSK
During the course of your application you will be requested to complete voluntary information which will be used in monitoring the effectiveness of our equality and diversity policies. Your information will be treated as confidential and will not be used in any part of the selection process.
If you require a reasonable adjustment to the application / selection process to enable you to demonstrate your ability to perform the job requirements please contact 0808 234 4391. This will help us to understand any modifications we may need to make to support you throughout our selection process.
Closing date for applications: April 20th 2018
When applying for this role, please use the 'cover letter' of the on-line application to describe how you meet the competencies for this role, as outlined in the job requirements above.
The information that you have provided in your cover letter and CV will be used to assess your application.
Thank you for your interest in this opportunity.
- Advanced degree in chemical, mechanical or biomedical engineering, biochemistry, physics, applied mathematics, bioinformatics, scientific computing or related field and strong experience in applying mechanism based mathematical modeling techniques in pharmaceutical industry (e.g. QSP or Physiologically Based PharmacoKinetic (PBPK) modeling)
- Core competency in numerical analysis focusing on differential equations and at least some experience in one of the following: finite element, computational fluid dynamics, numerical optimization, agent based modeling, stochastic differential equations, advanced image analysis
- Experience at building mathematical models to integrate available data and applying these to answer practical questions and have a positive impact on projects
- Scientific computing and programming - in particular Matlab, Simbiology and Comsol
- Familiarity with the challenges of drug discovery and forward thinking with respect to the general application of mathematical models in discovery and development
- Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in systems modeling
- Evidence of strong critical thinking skills, troubleshooting and problem solving skills
- Demonstrated collaborative behaviors both within the computational community and with the modeling customers in pharma
- Successfully leads in a matrix environment, working across functions/disciplines
- PhD degree strongly preferred
- Experience building and using Quantitative Systems Pharmacology/Toxicology models
- Evidence of broad knowledge and deep understanding of drug delivery, absorption, distribution and metabolism, pharmacology and toxicology, PK/PD modeling, physiological based modeling as well assystems biology
GlaxoSmithKline (GSK) is a world leading research-based pharmaceutical company that combines both individual talent and technical resources to create a platform for the delivery for strong growth in a rapidly changing healthcare market. Our mission is to improve the quality of human life by enabling people to do more, feel better and live longer.
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