Master Thesis Neural Network-Based Surrogate Models for EHL Contact Simulations

Join our innovative team and collaborate with leading researchers to develop high-end simulation tools used in the design and development of critical components for market applications. You will work within a dynamic team of experts gaining deep insights into our proprietary simulation software for contact dynamics.

• Your primary focus will be on enhancing our models and methods to significantly increase the speed quality and efficiency of these simulations through the application of advanced Machine Learning (ML) methods.
• This thesis offers you a unique opportunity to apply cutting-edge ML techniques to real-world engineering challenges gaining invaluable experience in both research and industrial application and contributing directly to critical product development.
• In addition you will develop novel algorithms and models to improve the performance scalability and efficiency of our simulation processes.
• Furthermore you will conduct in-depth analysis and interpretation of extensive simulation output datasets to extract meaningful information which will be used to train and validate your algorithms.
• You will make use of Machine Learning techniques ranging from advanced Gaussian Optimization to deep Neural Networks to develop comprehensive design models based on our extensive simulation data.
• Last but not least you will generate robust surrogate models test and demonstrate their efficiency improvements and benefits in real design situations and industrial applications.

Qualifications :

• Education: Master studies in the field of Engineering Computer Science Applied Mathematics or comparable in Science or Engineering
• Experience and Knowledge: proficiency in programming languages particularly Python; strong background in AI Machine Learning and optimization methods; experience with PyTorch and/or TensorFlow is desirable
• Personality and Working Practice: you are a self-starter who can work effectively both independently and as part of a team proactively identifying challenges and proposing innovative solutions; you possess a structured and organized approach to research combined with excellent analytical and critical thinking skills
• Work Routine: partially mobile working is possible though on-site discussions and collaboration are expected
• Enthusiasm: a passion for Machine Learning programming and a problem-solving mindset
• Languages: very good in German or English

Additional Information :

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.

Need further information about the job
Cesar Pastor (Functional Department)
49 2

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Remote Work :

No

Employment Type :

Full-time