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Internship in MEMS Packaging Simulation – Credibility and Numerical Uncertainty Quantification(m/w/x)
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.
Anforderungen
- 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
Aufgaben
- 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
Ausbildung
Sprachen
Tools & Technologien
- Bosch Sensortec GmbHVollzeitWerkstudentnur vor OrtReutlingen
- Bosch Sensortec GmbH
Praktikum akustische Charakterisierung von MEMS(m/w/x)
VollzeitPraktikumnur vor OrtKusterdingen - Bosch Sensortec GmbH
PreMaster Program | MEMS Sensor Measurement(m/w/x)
VollzeitWerkstudentnur vor OrtReutlingen - Bosch Sensortec GmbH
PreMaster Program | Software Engineer for MEMS Sensors(m/w/x)
VollzeitPraktikumnur vor OrtReutlingen - Bosch Sensortec GmbH
PreMaster Program | Software Engineer for Virtual Sensor Development Environment(m/w/x)
VollzeitWerkstudentnur vor OrtReutlingen
Internship in MEMS Packaging Simulation – Credibility and Numerical Uncertainty Quantification(m/w/x)
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.
Anforderungen
- 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
Aufgaben
- 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
Ausbildung
Sprachen
Tools & Technologien
Über das Unternehmen
Bosch Group
Branche
Manufacturing
Beschreibung
Das Unternehmen entwickelt hochwertige Technologien und Dienstleistungen, die das Leben der Menschen verbessern.
- Bosch Sensortec GmbH
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