Open Postdoctoral position, faculty mentor Barbara Simpson

Important Info

Faculty Sponsor First name: 
Barbara
Faculty Sponsor Last Name: 
Simpson
Stanford Departments and Centers: 
Civil and Environmental Engineering
Postdoc Appointment Term: 
Initial appointment is 1 year with the expected renewal after the first year for an additional 1-2 years by mutual agreement.
Appointment Start Date: 
Applications will be considered on a rolling basis with start dates as early as 1/1/2026 and as late as 3/1/2026.
How to Submit Application Materials: 

Please upload all application material to: https://forms.gle/Goi1LE52hYk9otMQ8

Does this position pay above the required minimum?: 
Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.
Pay Range: 
77,000-80,000

A postdoctoral scholar is sought for a 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. The work will play an important role in the development of a subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due to fishing gear and secondary entanglement due to marine animals. Additionally, the digital twin will be utilized to monitor the structural health of deep-water mooring lines. 

Description: Extraction of abnormal entanglement from normal platform motion and sea states is difficult. Entanglement data can be noisy and notoriously difficult to process in remote, deep ocean environments. Moreover, continuous spatial and temporal monitoring results in enormous volumes of data, necessitating the management of large datasets and communication of extensive (and expensive) data packets. These factors necessitate new developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms for entanglement detection and classification. The algorithms are supported by affordable, spatial surface and subsea sensing and a low-power edge computing to collect and compress datasets at-sea, allowing the system to make critical inferences and reliably transmit data. Detection accuracy is then enhanced using a sub-sea digital twin, which contextualizes the data in terms of physical principles and outputs information to detect abnormalities associated with entanglement and changes to structural integrity. 

For further details on the research, scope, and position expectations please contact Dr. Barbara Simpson. This project includes collaborations with Sofar Ocean https://www.sofarocean.com/. Additional details on the research team can be found at: https://simpsoba.su.domains/ (Dr. Simpson’s webpage). The original call for the solicitation can be found here: https://www.energy.ca.gov/solicitations/2025-02/gfo-24-307-advancing-designs-and-analysis-high-voltage-direct-current

Required Qualifications: 
  • Doctoral Degree (PhD, MD, or equivalent) conferred by start date.
  • Demonstrated experience/knowledge of floating offshore wind, mooring line dynamics, digital twins, structural health monitoring, or real-time surrogate modeling
Required Application Materials: 
  • Curriculum vitae
  • Cover letter describing relevant experiences, interests, and goals
  • A scientific writing sample (e.g., manuscript or thesis chapter)
  • Contact information for 2 references 

Review of applications will begin immediately. Additional supporting information may be requested upon review. 

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.