Master’s Thesis: AI-Driven 3D Urban Wind Emulator

Fraunhofer Karriere; Arkansas; On-site; USD 11,160 - 22,320; Part time

Benefits offered by this job

Master's thesis

GPU resources

Mentorship

International collaboration

Job summary

Fraunhofer IBP offers a Master’s thesis opportunity at the intersection of AI and urban physics. You will work on neural operators, SDFs for geometry, and DL models to predict 3D wind and temperature fields in urban districts.

The role includes access to high-performance workstations and GPU resources, with mentorship from atmospheric modeling, meteorology and ML experts, and a collaboration with Concordia University in Canada.

Qualifications

• Enrollment in a relevant scientific computing or AI program.
• Strong Python and DL framework skills, or willingness to learn.
• Interest in geometric DL and fluid dynamics; willing to learn SDFs.

Responsibilities

• Assist with generating synthetic urban datasets using LES for training/validation.
• Implement Signed Distance Functions and gradients for building geometries as NN inputs.
• Develop and train DL models to approximate 3D wind and temperature fields in urban districts.
• Evaluate generalization capabilities of the model.
• Compare accuracy and inference speed against CFD results.

Skills

Python

Deep learning

PyTorch

TensorFlow

Geometric deep learning

PDEs

Fourier analysis

Education

Master's student in Scientific Computing/CS/Physics/Geoinformatics/Meteorology

Tools

PyTorch

TensorFlow

Job description

Fraunhofer IBP offers a Master’s thesis opportunity at the intersection of AI and urban physics. You will work on neural operators, SDFs for geometry, and DL models to predict 3D wind and temperature fields in urban districts.

The role includes access to high-performance workstations and GPU resources, with mentorship from atmospheric modeling, meteorology and ML experts, and a collaboration with Concordia University in Canada.