PhD Candidate - Physics-Based Learning under Numerical and Epistemic Uncertainty in Heterogeneous Engineering Simulation Data

We are seeking a PhD candidate to work on physics-based learning under numerical and epistemic uncertainty in heterogeneous engineering simulation data. The successful candidate will work in the area of virtual vehicle safety testing (Finite Element Method, FEM, full vehicle crash) and contribute to the digital transformation through the application of modern AI methods.

Responsibilities:

• Research and develop mathematical and algorithmic methods for large physics models in virtual vehicle development
• Work with historically grown and heterogeneous CAE data, considering different modeling approaches, vehicle projects, and development maturity levels
• Develop methods for integrating physical laws and numerical procedures into AI model architectures and training processes
• Create robust representations of physical relationships and investigate their transferability to different modeling approaches, numerical methods, and development maturity levels
• Explore procedures for quantifying data, model, and prediction uncertainties to evaluate the reliability of predictions and support informed decision-making

Requirements:

• Master's degree in Mathematics, Applied Mathematics, Computer Science, or a related field
• Strong knowledge of numerical mathematics
• Initial experience in machine learning, data analysis, and statistics
• Programming skills, particularly in Python
• Enthusiasm for scientific work and the development of new mathematical methods
• Structured, independent, and solution-oriented work with high quality standards
• Excellent team and communication skills
• Ideally, experience with large and heterogeneous real-world data sets
• Ideally, knowledge of numerical simulation, particularly Finite Element Method (FEM)

Benefits:

• Comprehensive and flexible old-age provision and insurance options
• Various offers to support work-life balance, including childcare, holiday programs, and parent networks
• Attractive conditions for purchasing and leasing vehicles
• Diverse, subsidized meal options
• Sustainable mobility options, including discounted Germany tickets, bicycle leasing, and comprehensive charging infrastructure for electric vehicles
• Corporate benefits and employee discounts on Porsche lifestyle products and services