PhD Thesis: Physics-Informed Predictive Models for Engineering Systems using Simulation and Sensor Data
Mode of Employment: Fixed Term / Part-Time; (20 hours / week)
Are you passionate about merging physics, machine learning, and engineering to shape the future of predictive modeling? Join us in Garching as a PhD candidate (f/m/d) and drive innovation at the intersection of simulation, sensor data, and AI for next-generation engineering systems!
What we offer you
- Attractive remuneration package
- 30 leave days and a variety of flexible working models that allow time off for yourself and your family
- Share matching programs to become a shareholder of Siemens AG
- Continuous Learning: Benefit from specialized training and daily challenges to keep your expert knowledge up-to-date
- Innovative Environment: Be part of a team that values innovation and continuous improvement
Since each of over 300,000 employees feels that other benefits are particularly important, and we cannot list our entire benefit portfolio here, you can find more information here.
The individual benefits are subject to regulatory, contractual, or corporate conditions.
You'll make an impact by
- You develop physics-informed predictive models that combine simulation data with real-world sensor inputs to accurately represent complex engineering systems
- Building on this, you implement fast surrogate and reduced-order models to enable efficient simulations and analyses
- You investigate advanced machine learning methods such as Neural Operators and Operator Inference, with the aim of improving model generalization and performance
- Additionally, you address the challenges of transferring models from simulation to real-world applications (Sim2Real), ensuring robust performance even with noisy, sparse, or incomplete sensor data
- To ensure reliability, you integrate uncertainty quantification (UQ) methods into your models, providing trustworthy and interpretable predictions
This is how you'll win us over
- Education: You hold a Master’s degree in a relevant field such as mathematics, engineering, physics, or computer science.
- Experience and Skills:
- You have experience with physics-informed predictive modeling for engineering systems
- You are skilled in combining simulation and sensor data, including handling noisy, sparse, and incomplete datasets
- You possess knowledge of neural operators, operator inference, and uncertainty quantification for reliable predictions
- Ways of Working: You work analytically, independently, and demonstrate strong problem-solving abilities
- Languages: Very good English skills are required, German is recommended
You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know you!
At Siemens, we believe that feeling valued and included is the foundation for doing great work. That’s why we aim to create an inclusive workplace where everyone feels a sense of belonging, and where individual perspectives and experiences are celebrated. Our commitment to fairness and respect extends to every applicant. As an equal opportunity employer, we welcome applications from individuals of all backgrounds and particularly encourage applications from persons with disabilities.
About UsThe world never stands still. And new challenges arise every day. With a passion for questioning things, for supplying ideas, and intelligently driving things forward we are helping society move towards a smarter tomorrow. Be it with technologies that reduce carbon emissions in cities or hyperintelligent robots. This is how we are able, to tackle the most important projects and push them forward together. Help us shape the future.
www.siemens.de/careers – if you would like to find out more about jobs & careers at Siemens.
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