PhD Studentship: Maximising Performance with Scientific Machine Learning (AE0078v2) - OB
Core
Investigate the two-way interaction between offshore wind farms and marine stratocumulus clouds to optimize wind farm performance using scientific machine learning.
Role type
PhD Researcher in Scientific Machine Learning for Aeronautics
Builds
Scientific machine learning strategies for flow physics discovery and real-time optimization of wind farm operations
Domain
Aeronautics / Renewable Energy / Atmospheric Physics
Deliverable
production ML models
Required skills
Large eddy simulation (LES) code usage, scientific machine learning, real-time optimization, actuator disc/line wind-turbine modeling, data assimilation, reduced order modeling
Preferred skills
Self-similarity law discovery, quantized local reduced order models
Technologies
Large eddy simulation (LES), scientific machine learning tools, real-time optimisers
Responsibilities
Simulate wind farms exposed to various atmospheric inflows, develop SML-based strategies for flow physics discovery, implement actuator disc/line wind-turbine models, collaborate with ERC projects on turbulence and wind-energy flows
Seniority
PhD Candidate