Deep Learning Researcher

13 ago - Italia
Siemens Digital Industries Software

We are Siemens

Siemens Digital Industries Software is a leading provider of solutions for the design, simulation, and manufacture of products across many different industries. Formula 1 cars, ships, space exploration vehicles, and many of the objects we see in our daily lives are being conceived and manufactured using our software.
Are you a Deep Learning Researcher who possess expertise in both deep learning and physics-based simulation? The PhysicsAI Team at Siemens is leveraging AI to reinvent the product design cycle. With a focus on customer impact and a strong culture of collaboration and innovation, our team is harnessing emerging technologies like 3D genAI and geometric deep learning to enable faster product design. The team’s expertise ranges from traditional engineering design and simulation, deep learning, computational geometry, design space exploration, and user experience.
If you’d like to be responsible for designing and investigating new methods for DL-based surrogate modelling and generative AI, testing them on industrial problems,



and implementing them into Simcenter PhysicsAI then we’d love to hear from you
You’ll make a difference by:
- Identifying and investigating emerging deep learning technologies and their application to mechanical, aerospace, and civil engineering.
- Taking the most promising approaches all of the way to productization by writing high quality production code.
- Assessing opportunities and gaps in the current genAI technology to inform future methods development.
- Capturing customer needs for complex engineering design problems by working with application engineers and specialists.

Your success is grounded in:
- Master’s or PhD in Computer Science, Engineering, Artificial Intelligence, Mathematics, Physics, or a related technical field
- Strong proven theoretical knowledge of deep learning fundamentals
- Strong familiarity with architectures like transformers, diffusion models, normalizing flows, and Graph Neural Networks

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