Overview

Title: Quantitative Systems Pharmacologist / Systems Biology Scientist
Deep Origin is a biotechnology company accelerating drug discovery through AI-powered tools. Our platforms simplify R&D, simulate biology, and empower scientists to solve diseases and extend healthspan.
We are looking to recruit a Scientist with experience applying Systems Biology modeling and concepts in a Quantitative Systems Pharmacology/Toxicology context. You will construct organ and tissue models that connect molecular-level interactions to physiological and toxicological endpoints and outcomes.
For US applicants: all applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
– PhD in Bioengineering, Biotechnology, Systems Biology, QSP/PKPD, Systems Pharmacology, etc.
Nice to have:Benefits Section:

Responsibilities:
  • 2 or more years of postdoctoral or industry experience constructing systems biology and related models in a QSP/QST context. Particularly in one or more of Liver, Intestine, Kidney, Blood, and Bone marrow.
  • Extensive experience in Python (at least 3 years).
  • Experience with SBML (2 years).
  • Fluent English for collaboration with an international team.
  • Be able to work on US time zones when needed.
  • Construct Systems Biology-oriented QSP/QST models that capture molecular interactions and link these to physiological and toxicological outcomes and phenotypes.
  • Work with the Deep Origins Cellular Simulations team and the wider company to interface these models with larger-scale intracellular models and multi-scale models of biological processes relevant to physiology and toxicology.
  • Plan and organize work to ensure specific deadlines and milestones are met, coordinating with others to ensure work is correctly aligned and integrated with other efforts.
  • Communicate effectively within the company and external teams, updating others frequently on progress and bottlenecks.
Required Qualifications:
  • Deep knowledge of available datasets to inform QSP/QST models.
  • Experience deploying models in an HPC environment.
  • Experience in model parameter optimization.
  • Experience in virtual population generation.
  • Composite model construction, parameterization, and simulation.
Note:

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