Our mission at Asimov is to advance humanity's ability to design living systems, enabling biotechnologies with outsized benefit to society. We're developing a mammalian synthetic biology platform––from cells to software––to enable the design and manufacture of next-generation therapeutics.
We are searching for a Senior Computational Biologist with multi-disciplinary skills in computational biology, machine learning, and applied mathematics to join our team. This role bridges the gap between sequence-level design and systems-level dynamics, focusing on the development and application of sophisticated models that integrate diverse biological data.
The ideal candidate will be adept at building data-driven, mechanistic, and hybrid models to decipher complex biological processes and guide the engineering of therapeutic molecules and systems. You will leverage everything from high-dimensional omics datasets to time-course bioprocess data to create predictive models that drive genetic design and optimize bioprocess development. Join our team to contribute at the cutting-edge of innovation in biology and computational science, shaping the future of therapeutic innovation.
About the Role:
- Design and implement sophisticated models—including data-driven (ML/AI), mechanistic (e.g., ODE-based), and hybrid approaches—to predict and optimize the performance of biologics and vector manufacturing processes.
- Develop models that predict functional outcomes such as transgene expressibility, protein stability, and product quality attributes from nucleic acid and amino acid sequence features
- Develop new models to predict developability attributes such as expressibility, secretability, aggregation, and stability of biologics, from sequence.
- Create multi-scale models of cellular behavior, integrating omics data (RNA-seq, proteomics, etc.) to simulate gene expression, protein secretion dynamics, metabolism, and cell phenotype.
- Work in close collaboration with bench scientists to guide rational experimental design, using techniques like Bayesian Optimization to ensure that data generation is maximally informative for modeling and yields actionable insights.
- Contribute to our long-term computational strategy by exploring novel algorithms, modeling frameworks, and data integration techniques to solve critical challenges in synthetic biology and therapeutic development.
About You:
- You have a Ph.D. in Bioengineering, Computational Biology, Computer Science, Applied Mathematics, or a related quantitative field.
- You are proficient in sequence-based analysis and have hands-on experience integrating large-scale omics datasets (e.g., NGS, RNA-seq, proteomics) to inform model development and validation.
- You are an expert in applying machine learning and advanced mathematical techniques (e.g., dynamical systems, differential equations, stochastic modeling) to biological systems, with at least 3 years of experience in a pharmaceutical or biotechnology industry setting.
- You have a strong background in modeling dynamic biological systems, such as gene regulatory networks, metabolic pathways, or cell population dynamics.
- You are a skilled programmer, fluent in Python and associated scientific computing/ML libraries.
- You excel at communicating complex scientific and mathematical concepts effectively to colleagues with diverse backgrounds and expertise.
- You are a collaborative, impact-driven scientist, passionate about working in a fast-paced, research-focused environment to engineer biology.
We're fueled by a vision to transform biological engineering into a fully-fledged engineering discipline. Should you join our team, you will grow with a constantly evolving organization and push the frontiers of synthetic biology. Company culture is key to Asimov, and ours is a culture of recombination; we believe that our mission can only be achieved by bringing together a diverse team with a mixture of backgrounds and perspectives.
