Scientist, Multiscale Modeling of Cellular Processes

What You Will Contribute To Altos

The Multiscale Modeling Group in the Computational Innovation Hub at Altos Labs is seeking an independent and highly motivated candidate to build, maintain, and support our modeling and optimization frameworks to model cellular processes. The position will work in a multi-disciplinary research environment to assist investigators in algorithm development and implementation, and will work across the software development life cycle, including software design, coding, testing, deployment, and maintenance. The role will also support lab operations and scientific outreach activities as necessary. The ideal candidate will be a good communicator and have a growth mindset with an enthusiasm for scientific discovery and strong interest in biological applications.

The Multiscale Modeling Group works closely with the other members of the Computational Innovation Hub and collaborates toward understanding mechanistic aspects of cellular processes with all experimental labs at Altos Labs. The group is particularly interested in systems biochemistry and molecular biology of reprogramming, a dynamic field that seeks to understand complex biological systems by integrating data about biochemical components and help design interventions that direct cellular states along desired trajectories. This includes working with quantitative “big data” and building predictive models to develop novel theories. Our ultimate goal is to contribute to improving human lives through better-informed treatments.

Key Functions:

• Contribute to modeling, simulation, and analysis of mechanistic or data-driven models of biological processes through software development in a highly collaborative environment.
• Design and implement pipelines to mechanistically model cellular processes from model instantiation to simulation, calibration, and analysis.
• Collaborate with other scientists to characterize model behaviors, predictions, refinements, and accelerate the biological knowledge discovery.
• Influence best practices in areas such as Bayesian optimization, causal inference, building and assessing predictive models, analyzing biological networks, and analysis and visualization of -omics data.
• Maintain documentation and keep up-to-date as needed.
• Contribute to software releases (e.g. via GitHub, PyPI, Anaconda, Docker Hub).

Who You Are

The ideal candidate will be a strong collaborator with a background in biostatistics (including Bayesian inference), with some AI experience and a working understanding of cell biology and/or biophysics.

Minimum Qualifications:

• PhD in Systems Biology, Biophysics, Bioinformatics, or closely related field with strong emphasis in biological modeling.
• Solid grounding in statistics theory and familiarity with recent work in statistics.
• Familiarity with Bayesian statistics, semi- and non-parametric estimation and inference, multivariate methods, and experiment design.
• Working knowledge of cell biology.
• Experience with Python, C, R or related scientific computing languages.
• Strong problem-solving skills and collaboration skills coupled with rigorous, creative thinking.
• Enjoy collaboration to solve challenging problems at the intersection of physics, chemistry, and cell biology through the use of numerical methods.
• In-depth knowledge of one or several of the following areas: Bayesian statistics, (regularized) models, network analysis, multi-omics data integration.
• Strong written and oral communication skills, with experience presenting and communicating research work.
• Ability to work in a team setting or independently as needed to contribute to ongoing projects.

Preferred Qualifications:

• Experience with machine learning / deep learning.
• Proven publication record in the general area of systems biology.
• Experience with building and deploying software on GitHub, PyPI, Anaconda Cloud, and Docker Hub.
• Knowledge of parallel computing technologies, such as NVIDIA’s CUDA platform, OpenCL, and OpenMPI.

#LI-JM1