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Mountain View, CA, USA
2026 PhD Residency, Software Engineering-Socio-Environmental Risk Modeling (Tapestry)
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About the role
This 6-month PhD program focuses on building software systems that integrate economic, environmental, regulatory, and public-perception data to model non-technical risks in large infrastructure projects. You will design and implement data pipelines, analytical models, and prototypes that transform diverse data sources into actionable risk insights.
This is a hands-on role with strong analytical depth. We are looking for someone who is interested in the intersection of data-heavy applications, high-performance engineering, and the responsible, intentional application of technology to navigate societal complexity and global problems.
How you will make 10X Impact
- Bridge technical and human context: Navigate the nuance between hard data and social impact, ensuring that software models remain grounded in real-world consequences and ethical data stewardship.
- Enable earlier decision-making: Build software pipelines that surface non-technical risks earlier in the project lifecycle, reducing costly downstream surprises.
- Unify fragmented data: Integrate structured and unstructured data sources into a cohesive, analyzable system.
- Improve modeling speed: Develop efficient workflows that accelerate experimentation and iteration across economic and policy-related signals.
- Increase interpretability: Translate complex analytical outputs into clear, explainable insights through well-designed interfaces or prototypes.
- Support real-world outcomes: Help inform decisions that balance cost, environmental considerations, and public acceptance.
What you should have:
- PhD student in Computer Science, Software Engineering, Data Science, Economics, or a related field
- Strong proficiency in Python, Java, or Kotlin for data processing and modeling
- Experience building data pipelines, APIs, or analytical workflows
- Familiarity working with large, messy, or unstructured datasets
- Ability to design maintainable, well-structured software systems
- Strong analytical thinking and problem-solving skills
It’d be great if you also had one or more of these:
- Experience with natural language processing or text analysis
- Exposure to geospatial or spatially indexed data
- Familiarity with statistical or econometric modeling concepts
- Experience building dashboards or lightweight data visualizations
- Interest in applied research that bridges analysis, software systems, and responsible application of technology to complex socio-environmental ecosystems and large-scale corporate frameworks