Doctoral Researcher in Physics-Enhanced Machine Learning for Floating Offshore Wind Digital Twins
Core
Develop physics-enhanced machine-learning models for digital twins of floating offshore wind turbines to estimate unmeasured dynamics, detect degradation, and quantify uncertainty.
Role type
Doctoral Researcher (Research IC)
Builds
Digital twins for floating offshore wind turbines
Domain
Offshore wind energy, marine hydrodynamics, machine learning
Deliverable
research
Required skills
Python, machine learning, linear algebra, hydrodynamics, structural dynamics, signal processing, Bayesian filtering, uncertainty quantification
Preferred skills
OpenFAST, dynamic mode decomposition, Koopman operator theory, CFD, deep learning for time series, scientific publications
Technologies
Python, PyTorch, JAX, OpenFAST, WAMIT, MoorDyn, OpenFOAM, REEF3D
Responsibilities
Develop physics-enhanced ML models for coupled hydrodynamic and structural dynamics; Embed hydrodynamic knowledge into models; Estimate unmeasurable wave-induced motions and mooring loads; Detect early structural degradation; Quantify estimation uncertainty; Generate and analyze data from hydrodynamic simulations; Publish results in journals and conferences
Seniority
PhD candidate (Researcher)