Research Intern, Systems Biology
Our mission is to make biology easier to engineer. Ginkgo is constructing, editing, and redesigning the living world in order to answer the globe’s growing challenges in health, energy, food, materials, and more. Our bioengineers make use of an in-house automated foundry for designing and building new organisms.
The Systems Biology Team works to address the complex challenge of engineering metabolic networks. We utilize state-of-the-art approaches to generate predictions that inform metabolic engineering projects and collaborate with wet lab teams to integrate these predictions with experimental data.
As a Research Intern, Systems Biology, you will help develop in silico models for rational metabolic engineering and apply interdisciplinary technologies to address the complex challenge of metabolic engineering. You will support Ginkgo’s Design-Build-Test-Learn cycle by building software to integrate data with model predictions. We are looking for someone who is excited about the promise of synthetic biology and the premier role of biomolecular engineering in biology by design. If you view life as a series of optimization problems, then you are at the right place.
Projects will be focused on learning, using, and improving a core metabolic modeling workflow. As a Research Intern in Systems Biology, you will be trained on the current workflow, and implement improvements in two categories: (1) Scientific improvements, e.g. adding modules for new modeling or analysis capabilities and (2) Code improvements, i.e. documentation, testing, etc. As such you will develop multiple important skills in computational systems biology, across both science and coding. We expect that most candidates will have more prior expertise in one or the other, and we are happy to tailor our training accordingly!
The Ginkgo Bioworks Early Talent program is open to students who will return to their degree programs upon completion of their employment at Ginkgo. Candidates must be enrolled in a United States based institution and/or have work authorization in the United States to be eligible to participate.
- Metabolic modeling: Apply flux-balance analysis to metabolic engineering projects. Develop pipelines to facilitate the use of next-generation models in a fast-paced metabolic engineering environment.
- Biological data processing: Engineer data flows to ensure that model predictions can be compared to experimental data. Decipher biological data complexity to highlight key features that will help teams reach milestones quicker.
- Streamline workflows: This project will be focused on learning, using, and improving a core metabolic modeling workflow. The intern will be trained on the current workflow, and implement scientific improvements (e.g. new modeling or analysis capabilities) and code improvements (e.g. new features implementation, documentation, testing)
- Interdisciplinary research: As such the candidate will develop multiple important skills in computational systems biology, across both science and coding. We expect that most candidates will have more prior expertise in one or the other, and we are happy to tailor our training accordingly
- Currently enrolled in a bachelor’s degree (current undergraduate junior at the time of application preferred) or currently enrolled in a master’s degree program
- Majoring or pursuing degree in computational biology, computer science, chemical or biological engineering, biochemistry, molecular biology, structural biology, biophysics, physics, quantitative biology, or related field
- Experience with at least one software programming language (Python is preferred).
- Familiarity with developing quantitative models for interpretation of biological data, and applying the learnings to generate hypotheses and testable predictions, a plus.
- Enthusiasm to learn about biological modeling and its application in an industrial setting.
- Strong curiosity of areas of biology previously unknown to you.
Preferred Capabilities and Experience
- Exposure to at least one type of metabolic modeling software environment (i.e. COBRA, RAVEN) is a plus.
- Experience in visualizing and analyzing metabolic networks
- Experience with machine learning for data analysis is a plus.
- Wet lab skills (or knowledge of what a wet lab looks like) a plus.
We also feel that it’s important to point out the obvious here – there’s a serious lack of diversity in our industry, and that needs to change. Our goal is to help drive that change. Ginkgo is deeply committed to diversity, equity, and inclusion in all of its practices, especially when it comes to growing our team. Our culture promotes inclusion and embraces how rewarding it is to work with people from all walks of life.
We’re developing a powerful biological engineering platform, so we must remain mindful of the many ways our technology can – and will – impact people around the world. We care about how our platform is used, and having a diverse team to build it gives us the best chance that it’s something we’ll be proud of as it continues to grow. Therefore, it’s critical that we incorporate the diverse voices and visions of all those who play a role in the future of biology.
It is the policy of Ginkgo Bioworks to provide equal employment opportunities to all employees and employment applicants.