We believe that diverse perspectives are foundational to scientific innovation and inquiry.
We are building a company where exceptional scientists and industry leaders from around the world work side by side to advance a shared mission.
Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives.
At Altos, we are all accountable for sustaining a diverse and inclusive environment.
What You Will Contribute to Altos
The Altos Computer-Aided Reprogramming group works at the complex interface between mathematics, physics, computer science and bioengineering, with the aim of establishing design principles of reprogramming for rejuvenated cells. Responsibilities include:
Developing research programs on partial cellular reprogramming, with the intent of closing the feedback loop between experimental and theoretical work, at multiple scales, from molecules to cells, tissues and even whole organisms.
Working at the interface between mathematical and computational models, and AI methods, with the aim of establishing design principles of rejuvenated cells.
Collaborating with both experimental and computational scientists across Altos.
Supervising and training junior and mid-career scientists in the group.
Who You Are
This position can be filled at the Scientist, Senor Scientist or Principal Scientist level, depending on the qualifications of the selected candidate.
PhD in Physics, Mathematics, Bioengineering or related discipline.
5+ years of experience in the application of mathematics and physics to problems of biological interest.
Experience using dynamical systems, control theory or other theoretical frameworks to model biological systems.
Demonstrated experience with advanced data driven modeling tools (including neural networks, autoencoders or generative models).
Expertise and a track record of using methods from artificial intelligence for biological design.
Record of applications of dynamical systems to problems of synthetic biology.
Record of applications of data driven modeling methods and AI to synthetic biology.