Ph.D. in a scientific field, preferably computational life sciences, computational molecular biology, computer aided drug discovery and/or statistical modeling.
Proficient in one or more programming languages (e.g. Perl, C/C++, Java or Python, Ruby), and competent working in a Linux/Unix environment
Experience in the prediction of one or more aspects of human pharmacokinetics and/or toxicology as it relates to human therapeutics.
Inquisitive, creative and bold. Disruptive and innovative thinker that can deliver results.
Excellent written and oral communication skills and the ability to interact effectively outside of the individual’s discipline.
An ability to work and thrive in an extremely energetic, intellectual, fast-paced and matrix environment. Excellent interpersonal abilities, demonstrated inclusivity, flexibility and a history of working collaboratively in a team environment.
Comfortable with risk, willing to fail, learn from it, and move on.
High learning agility and a willingness to contribute at all levels of the ATOM group, including areas outside of the individual’s expertise.
Familiarity with deep/machine learning and other data driven mathematical and modeling techniques, and their application to pharmaceutical data sets.
Knowledge of the pharma industry and/or the development of human therapeutics.
Experience in computational chemistry, computational molecular biology and molecular simulation.
GSK recently declared its intention to create the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium with the National Cancer Institute (NCI) and the U.S Department of Energy (DOE). This consortium aims to dramatically accelerate the discovery and development of drugs with an aspiration to go from target to first in human precision trials in twelve months or less. GSK, NCI, and DOE hope to achieve this goal by applying high performance computing modeling and simulation to large datasets that include information about structure and properties of existing and potential drug compounds. As part of GSK’s commitment to this initiative, the company has created several post-doctoral opportunities to work alongside industry and government scientists and be placed at the ATOM facility in San Francisco, CA.
One of these postdoctoral positions will be for a computational scientist with experience in computational modeling of the pharmacokinetics and/or toxicology of human therapeutics. The incumbent will be part of a larger cross-functional consortium team at the ATOM facility, and together this team will be responsible for building and utilizing multi-scale mechanistic and data-driven models to predict the pharmacokinetics, efficacy & safety liabilities (on/off target) of small molecule therapeutic candidates in healthy and diseased human systems. The models are intended to be used in virtual screens, integrated into multi-parameter active-learning based drug development workflows, and utilized in the ongoing development and validation of a quantitative pharmacology model for individual cancer patients.
This is an exciting opportunity to participate in an ambitious collaboration that will integrate high-performance computing, diverse biological data, and new approaches to molecular discovery in an exciting and energetic facility in the heart of San Francisco. Ultimately, the enablement of precision and accelerated discovery of therapeutics will expedite our ability to discover and develop novel medicines that will help patients do more, feel better and live longer.
Prioritize and develop computational ADME models & technologies to accelerate the discovery and development of targeted therapeutics for Oncology with a high probability of success. If necessary, work with external partners to develop such models.
Provide high quality scientific and strategic guidance and recommendations to other ATOM scientists on complex in vitro models based on scientific merit, practicality and potential impact.
Collaborate with experimental scientists at the ATOM facility to further develop and validate mechanisms that will rapidly integrate advanced biological data into the active-learning process.
Work with the scientists throughout the ATOM facility to continuously optimize the Discovery process for generating therapeutics with a high probability of success in the shortest amount of time.
Advocate a culture of scientific challenge and peer review of existing complex in vitro models and of new technological opportunities to help ATOM eliminate the use of animal models and promote accelerated filing timelines.
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