Scientist, Modeling and Optimization
Role details
Job location
Tech stack
Job description
We are looking to recruit a modeling and numerical optimization specialist with experience working in Systems Biology, Quantitative Systems Pharmacology, and/or Toxicology. You will help to construct simulation and parameter optimization approaches for large-scale systems of biological models, which include mechanistic and machine learning model components, used in whole-human toxicology predictions. Additionally, you will work on efficiently simulating and optimizing the system of models at scale, including using high performance computing and distributed computing frameworks., * Construct software frameworks for seamlessly connecting, executing, and parametrizing large-scale systems of biological models, ranging from physiological to molecular scale.
- Work with the Deep Origins Cellular Simulations team and the wider company to develop interfaces for sub-models at various scales, which represent biological processes relevant to physiology and toxicology, to incorporate into the above framework.
- Incorporate interpretable machine learning methods in the system of models, where appropriate, to help capture unrepresented interactions and calibrate to experimental or clinical outcomes.
- 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.
Requirements
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Bachelor's or Master's in a relevant quantitative field (Biology, Computer Science, Math, Physics, Engineering, etc.).
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Experience in construction and parametrization of biological models, either ML or mechanistic.
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Extensive coding experience in Python.
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Experience with high-performance computing and/or distributed systems.
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Experience with optimization algorithms and numerical considerations.
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Experience with classical ML approaches, such as tree-based methods, MLPs, etc., * Experience with cellular pathway or organ modeling, for purposes of drug discovery or toxicology.
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Experience with creating surrogate models of biological systems, with analytical or ML-based approaches.
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Experience with interpretability and sensitivity analysis of mechanistic and machine learning models.
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Experience with GPU computation, C, and C++.