A doctoral candidate or postdoctoral scholar is sought for a funded research position in SEA-Scan: Structural and Environmental Algorithms for Sensing of Offshore Systems, working with Dr. Barbara Simpson in the School of Engineering and School of Sustainability, Department of Civil and Environmental Engineering at Stanford University.
This project will play a key role in developing a subsea digital twin of deep-water mooring lines for floating offshore wind turbines, with integrated machine learning–based detection of mooring line entanglement and structural health monitoring.
🔬 Research Scope
Detection of mooring line entanglement—whether primary entanglement from fishing gear or secondary entanglement from marine animals—is particularly challenging due to:
- Complex platform motion and variable sea states
- Noisy, sparse, and difficult-to-process subsea data
- Enormous volumes of spatial and temporal monitoring data
- Constraints on offshore power consumption and data transmission
To address these challenges, this project focuses on the development of machine learning algorithms for entanglement detection and classification, supported by:
- Affordable spatial surface and subsea sensing systems
- Low-power edge computing for at-sea data compression and inference
- A physics-informed subsea digital twin that contextualizes sensor data using structural mechanics and ocean engineering principles
The digital twin will enhance detection accuracy and provide insights into abnormal behavior, entanglement events, and changes in structural integrity of deep-water mooring lines.
🤝 Collaborations & Funding
This research is part of a larger, externally funded effort and includes collaboration with Sofar Ocean.
Additional information on the research group can be found on Dr. Simpson’s webpage:
https://simpsoba.su.domains/
The original funding solicitation is available here:
https://www.energy.ca.gov/solicitations/2025-02/gfo-24-307-advancing-designs-and-analysis-high-voltage-direct-current
🎓 Qualifications
Candidates should demonstrate:
- An excellent record of research and/or industry experience
- Strong written and oral communication skills
- Enthusiasm for interdisciplinary research
Experience or knowledge in one or more of the following areas will be viewed favorably:
- Floating offshore wind systems
- Mooring line dynamics
- Digital twins
- Structural health monitoring
- Real-time surrogate or physics-informed modeling
- Machine learning applied to engineering systems
📅 Appointment Details
- Start date: Rolling, as early as January 1, 2026, and as late as March 1, 2026 (flexible)
- Position type: PhD or Postdoctoral appointment
- Compensation: Competitive salary and benefits
- Additional support: Funding available for travel and training
📬 How to Apply
Please upload the following materials via the application form:
👉 https://forms.gle/Goi1LE52hYk9otMQ8
Required materials:
- Cover letter
- Curriculum vitae (CV)
- Sample publication(s)
- Contact information for at least two references
Review of applications will begin immediately. Additional materials may be requested during the review process.
About the Author
