Construct and validate Genome-Scale Metabolic models, integrate them into larger models, and apply them to toxicological questions.
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 constructing and applying Genome-Scale Metabolic models (GEMs) to mammalian systems. You will construct and validate GEMs across a range of cell and tissue types, and work with other members of the Cellular Simulation team to integrate them into larger composite models, with the aim of understanding and predicting toxicological responses.
Requirements
- PhD in Bioengineering, Biotechnology, Systems Biology, QSP/PKPD, Systems Pharmacology, etc.
- Experience constructing and applying Genome-Scale Metabolic models in a mammalian context.
- Extensive experience in Python.
- Experience with SBML.
- Fluent English for collaboration with an international team.
- Ability to work on US time zones when needed.
Nice to have:
- 2+ years of postdoctoral or industry experience constructing and applying Genome-Scale Metabolic models in a mammalian context.
- Application of GEMs in a QSP/QST/Toxicology context.
- ODE modeling and parameterization.
Responsibilities:
- Construct and validate GEMs of various cell and tissue types, work with other team members to integrate them into larger models, and apply them to questions of toxicology.
- Be the subject matter expert on metabolic modeling, ensuring the Cellular Simulations team stays at the forefront of current research.
- Plan and organise 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.
Top Skills
Python
Sbml
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