We offer the following research topic

Thesis - Prediction of urban noise using ray tracing methods 

Master Thesis

The main focus of the master thesis will be the development of a simulation tool that uses ray tracing for predicting noise levels in urban areas. The model will take into account the acoustic sources coming from electrical loading columns that produce noise through the fan unit. To compare the accuracy and efficiency of the model it is compared to FEM calculations coming from commercial tools. During your thesis you will get the chance of learning different state of the art ray tracing and FEM simulation tools.

YOUR RESPONSIBILITIES:

• Literature study
• Development of ray tracing modeling techniques
• Tool and performance study and comparison with reference FEM calculations
• Calculation of relevant urban noise predictors

YOUR PROFILE:

• Ongoing studies in the fields of civil engineering, mechanical engineering or technical physics
• Experience in transmission dynamic and FEM tools is appreciated
• Language: English or German 
• Finite Element Software Basic Knowledge

WE OFFER:

• You can write your thesis independently and receive professional guidance and support from our experienced employees.
• You will have the opportunity to exchange ideas with experts in the company and benefit from their expertise.
• Take the opportunity to immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.

The successful completion of the thesis is remunerated with a one-time fee of EUR €3,500.00 before tax.

You don't want to write your final thesis just for the books, then explore the mobility of the future together with us! Maybe you will be a part of it soon!

At AVL, we foster and celebrate diversity: We recognize that diverse ways of thinking are required to achieve our vision of a greener, safer, and better world of mobility. Different backgrounds, attitudes, interests, and experiences make us successful. As Equal Opportunity Employer we consider all qualified applicants without regard to ethnicity, religion, gender, sexual orientation or disability status.