This research focuses on advancing cutting-edge aerodynamic design methodologies
to significantly enhance wind energy harvesting in urban settings. The primary 
objective is to develop a high-fidelity CFD–machine learning (CFD–ML) framework 
capable of efficiently analyzing and optimizing rooftop aerodynamic duct 
structures for building-integrated wind energy systems. The aim is to push the 
boundaries of current technology by identifying optimal aerodynamic 
configurations that maximize wind capture efficiency and mitigate turbulence 
under diverse urban layouts and meteorological conditions. To achieve this, the 
project explores advanced machine learning approaches, including surrogate 
modeling and reinforcement learning, to accelerate CFD optimization and enable 
adaptive control strategies for complex urban wind conditions. From an 
industrial 
standpoint, the objective is to deliver a cost-effective and efficient solution 
that facilitates continuous decentralized power generation in densely populated 
urban areas.

More information about this position and the application procedure can be found 
here:
https://www.linkedin.com/posts/hamid-montazeri-879a892a_cfd-machinelearning-
fluidmechanics-activity-7380859412405624832-TP2I?
utm_source=share&utm_medium=member_desktop&rcm=ACoAAAY14Z0BJCLTHM4rFOHTDsQKhQF88
I
rCoC0