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Multiphysics Power Module Simulation & Digital Engineeringonsemi · Munich

Multiphysics Power Module Simulation & Digital Engineering

MunichOn-siteFull-time

AI Summary

Leads simulation-driven and AI-enhanced virtual product development for SiC and GaN power modules, including multiphysics modeling, surrogate models, digital twins and design optimization. Builds Python-based engineering tools and automated simulation workflows.

About this role

We are seeking a highly innovative and technically strong Senior Engineer to drive the next generation of virtual product development methodologies for advanced power semiconductor packages. This position combines multiphysics simulation, machine learning, digital engineering, and design optimization to accelerate the development of SiC and GaN power modules.

The successful candidate will lead the development of AI-enhanced simulation workflows, surrogate modeling methodologies, digital twins, and automated design environments that enable faster, more reliable, and physics-informed product development. Working across global R&D teams, the candidate will establish novel approaches that combine first-principles engineering models with machine learning and optimization techniques.

Qualifications

Required Qualifications

  • M.S. or Ph.D. in Electrical Engineering, Mechanical Engineering, Physics, Computational Engineering, Computer Science, or a related field.
  • Minimum 3 years of industrial or advanced research experience in simulation-driven product development.
  • Strong expertise in multiphysics simulation and numerical methods.
  • Experience with one or more simulation platforms like Ansys
  • Strong programming skills in Python.
  • Experience with machine learning and data-driven engineering methodologies.
  • Strong communication and collaboration skills in an international environment.

Preferred Qualifications

  • Experience in:
    • Power Electronics Packaging
    • SiC and GaN Technologies
    • Virtual Product Development
    • Digital Twins
    • Reliability & Lifetime Prediction
  • Record of technical innovation demonstrated through patents, publications, software development, or industrial deployments.

Responsibilities

Technical Leadership

  • Drive the development of simulation-driven design methodologies for power module technologies.
  • Define and implement next-generation virtual product development frameworks.
  • Develop technical roadmaps for AI-assisted engineering and simulation automation.
  • Act as a technical expert for digital engineering initiatives across global development teams.

Multiphysics Modeling & Simulation

  • Lead electrical, thermal, mechanical, and coupled multiphysics simulations of advanced power modules.
  • Develop and validate simulation methodologies for SiC and GaN package architectures.
  • Support technology development through predictive modeling and virtual prototyping.
  • Correlate simulation results with experimental data and reliability testing.

Machine Learning & Digital Engineering

  • Develop machine learning models for performance prediction, reliability estimation, and design optimization.
  • Create surrogate models, reduced-order models, and digital twins of power modules.
  • Investigate state-of-the-art AI methodologies

Design Space Exploration & Optimization

  • Lead multi-objective optimization studies covering performance, cost, reliability, manufacturability, and sustainability.
  • Develop automated design synthesis workflows for package architecture exploration.
  • Implement uncertainty quantification and robust design methodologies.

Automation & Software Development

  • Develop Python-based engineering tools and automated simulation workflows.
  • Interface simulation platforms, databases, and AI frameworks.

Contribute to scalable software solutions supporting engineering productivity.

Skills

AnsysDesign OptimizationDigital TwinsGaNMachine LearningMultiphysics SimulationNumerical MethodsPower Electronics PackagingPythonReduced-order ModelsReliability PredictionSiCSurrogate ModelingUncertainty Quantification

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