Deep Learning Researcher - PhysicsAI

Experteer Overview In this role you drive DL-based surrogate modelling and generative AI within Siemens PhysicsAI, translating research into production-ready solutions for industrial design problems. You work within a collaborative team focused on 3D genAI and geometric deep learning to accelerate product design. You tackle challenging engineering problems, assess new AI methods, and deliver production-grade code that integrates into Simcenter PhysicsAI. This is an opportunity to shape AI methods for engineering simulation at scale. Pay / Benefits * Identify and explore emerging deep learning techniques for mechanical, aerospace, and civil engineering problems * Advance promising approaches to production-ready implementations * Evaluate current genAI capabilities to guide future method development * Gather and translate customer needs by collaborating with application engineers and specialists Tasks * Master’s or PhD in a technical field (e.g., Computer Science, Engineering, AI, Mathematics, Physics) * Strong theoretical grounding in deep learning fundamentals * Familiarity with transformers, diffusion models, normalizing flows, and Graph Neural Networks * Knowledge of physics-based simulation (FEA, CFD) and PDEs with numerical methods * Experience with mesh-based processing and major DL frameworks (PyTorch or TensorFlow) * Experience working on large, complex code bases * Strong problem-solving and clear communication of technical results Key requirements * hybrid by default * health and wellness benefits * incentive compensation * global mobility * merit-based career growth * diversity & inclusion initiatives

Experteer Overview In this role you drive DL-based surrogate modelling and generative AI within Siemens PhysicsAI, translating research into production-ready solutions for industrial design problems. You work within a collaborative team focused on 3D genAI and geometric deep learning to accelerate product design. You tackle challenging engineering problems, assess new AI methods, and deliver production-grade code that integrates into Simcenter PhysicsAI. This is an opportunity to shape AI methods for engineering simulation at scale. Pay / Benefits * Identify and explore emerging deep learning techniques for mechanical, aerospace, and civil engineering problems * Advance promising approaches to production-ready implementations * Evaluate current genAI capabilities to guide future method development * Gather and translate customer needs by collaborating with application engineers and specialists Tasks * Master’s or PhD in a technical field (e.g., Computer Science, Engineering, AI, aaaa Overview Physics) * Strong theoretical grounding in deep learning fundamentals * Familiarity with transformers, diffusion models, normalizing flows, and Graph Neural Networks * Knowledge of physics-based simulation (FEA, CFD) and PDEs with numerical methods * Experience with mesh-based processing and major DL frameworks (PyTorch or TensorFlow) * Experience working on large, complex code bases * Strong problem-solving and clear communication of technical results Key requirements * hybrid by default * health and wellness benefits * incentive compensation * global mobility * merit-based career growth * diversity & inclusion initiatives