Scientist , Explanatory Modeling Continuum Space

Scientist I, Explanatory Modeling in the Continuum Space

The mission of Allen Institute for Cell Science is to develop a comprehensive and holistic approach to measure, describe, and model cell states and their dynamic changes over time. Our ultimate goal is to uncover the fundamental principles of multiscale, multicellular morphogenesis, including how groups of cells organize and achieve collective behaviors essential for life. Our approach encompasses multimodal data collection including live 3D time-lapse imaging, data analysis, theory, and predictions to understand cell states and cell state transitions in human induced pluripotent stem cell models.

As a division within the Allen Institute, the Allen Institute for Cell Science uses a team‑oriented approach, focusing on accelerating foundational research, developing standards and models, and cultivating new ideas to make a transformational impact on science.

We are seeking a highly motivated and goal‑oriented scientist to develop and implement multiscale models that explain how cellular events emerge from underlying processes given known physical and biological constraints. This role offers a unique opportunity to apply and integrate concepts of non‑equilibrium statistical mechanics and active matter to understand how human cells interact to build tissues and ultimately organs. Working with a team that includes computational biologists, simulation experts, and software engineers, this scientist will help build explanatory modeling frameworks that not only reproduce real cellular dynamics but also predict the outcomes of experiments not yet conducted.

This role will collaborate closely with the Institute’s technical and programmatic teams to achieve the Institute’s shared, programmatic objectives. The candidate will participate in manuscript preparation and will also actively collaborate and ideate with other members of biophysics and mechanobiology, who will help guide priorities and identify exciting new directions.

At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly. We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal‑opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.

• Generate biophysical models that test different biological hypotheses on how cells interact to self‑organize into tissues and organoids
• Develop robust and reproducible continuum and discrete mechanical models that predict the transport and self‑organization of cell populations
• Test, falsify and modify these models by working with other programs in the institute and comparing the predictions against microscopy and imaging data
• Develop computational frameworks for hybrid modeling of cell populations that combine findings from machine learning and other descriptive modeling results with explanatory models
• Develop and maintain custom scripts and software tools for modeling (Python proficiency required)
• Coordinate with other programs in the Institute to ensure that the modeling outcomes align with and contribute to the overall goals on the project and for seamless integration and sharing of resources and data
• Maintain rigorous quality control standards and thorough documentation
• Prepare written summaries and present activities internally and publicly
• Adherence to SOPs, GLPs and regulatory requirements

• PhD in applied mathematics, biophysics, bioengineering, cell biology or a related science or engineering degree; or equivalent combination of degree and experience required

• Ability to communicate complex concepts and connect them to biological problems
• Deep knowledge of continuum and statistical mechanics concepts and their application to biological systems
• Advanced proficiency with numerical algorithms for solving PDEs…