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BOBosch Group

Internship in MEMS Packaging Simulation – Credibility and Numerical Uncertainty Quantification(m/w/x)

Reutlingen
Full-timeInternshipOn-site
AI/ML
Data Science

Thermo-mechanical FEM modeling and surrogate model construction for MEMS packaging using virtual Design of Experiments. Proficient Python programming and PyMAPDL skills required. Focus on numerical uncertainty quantification with Ansys Mechanical.

Requirements

  • Master studies in Mechanical Engineering, Computational Engineering, AI, Data Science or comparable
  • Strong knowledge and experience with Ansys Mechanical
  • Solid knowledge of PyMAPDL
  • Proficient programming skills in Python
  • Firm understanding of statistics
  • Sound knowledge of machine learning methods
  • Understanding of simulation credibility and uncertainty quantification
  • Commitment and communicative teamwork skills
  • Independent and structured working style
  • Requirement for on-site presence
  • Very good English and beginner German skills

Tasks

  • Prepare and refine existing thermo-mechanical FEM MEMS models
  • Vary numerical modeling parameters using virtual Design of Experiments
  • Evaluate simulation results for warpage, stresses, and strain
  • Construct surrogate models to approximate simulation responses
  • Conduct sensitivity analyses on dominant numerical parameters
  • Quantify uncertainty propagation to assess simulation robustness

Education

Master's degree

Languages

EnglishBusiness FluentGermanBasic

Tools & Technologies

Ansys MechanicalPyMAPDLPython
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