Senior Computational Scientist, Modeling and Simulation

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 Scientist with multi-disciplinary skills in applied mathematics, machine learning, and computational biology to join our team. The role involves the development and application of sophisticated modeling tools, including mathematical, machine learning, and AI-based approaches, to drive genetic design and optimize bioprocess development for human therapeutics. The ideal candidate will be adept at formulating models and associated algorithms at the appropriate level of abstraction, effectively addressing the problem at hand within the constraints of available data and project timelines. Join our team and contribute at the cutting-edge of innovation in biology and computational science, shaping the future of therapeutic innovation.

About the Role:

• Machine Learning for Therapeutic Vectors: Develop and apply models to predict and optimize monoclonal antibody, cell and gene therapy, and RNA therapeutic vectors based on their sequence; for example improve expressibility, secretability, tissue specificity, or function.
• Simulate Cellular Physiology: Create models to predict and optimize the design and effect of biologic drugs incorporating factors such as gene expression and secretion dynamics, target tissue phenotype, and metabolism.
• Improve Biosynthetic Processes: Use computational models to improve the yield and quality of in vitro and in vivo biosynthetic processes including DNA and RNA synthesis.
• Optimize Biologics Manufacturing: Use data-driven and hybrid bioprocess models to enhance the performance of biologics and vector manufacturing processes.

About You:

• Advanced Mathematical Modeling: You’re an expert in applying advanced mathematical techniques such as dynamical modeling, machine learning, and AI algorithms to biological systems. You have experience (at least 3 years) applying computational methods to genetic design and bioprocess development in the pharmaceutical or biotechnology industry.
• Sequence-based Modeling: You have a strong background in sequence-based analysis and prediction of the effect of sequence on outcomes such as gene expression and secretion, tissue specificity, or function.
• Dynamical Modeling: You have a strong background in dynamical system modeling and simulation, particularly as applied to biologic systems such as gene expression and secretion dynamics, phenotypic shifts, and metabolism.
• Computational Scientist: You’re proficient in programming languages commonly used in computational science and biology (Python, Julia, C++, etc).
• Bioprocess Engineering: You have experience in designing and optimizing bioprocesses such as cell culture, DNA assembly, or in vitro transcription for the production of therapeutics and vectors.
• Data Analysis: You’re able to handle and interpret complex biological data sets to derive actionable insights for genetic design and process optimization.
• Independent and Novel Research: You have a Ph.D. or equivalent degree in Bioengineering, Biotechnology, Computational Biology, Computer Science, Engineering, or relevant related fields.
• Collaboration and Communication: You communicate scientific and mathematical concepts effectively to experts and non-experts alike, and have the ability to collaborate across multidisciplinary teams in a research-driven environment.

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.