Bosch Group
Master Thesis Neural Network-Based Surrogate Models for EHL Contact Simulations(m/w/x)
Full-timeInternshipWith Homeoffice
Renningen
New Job?Nejo!
The AI Job Search Engine
BO
Bosch GroupPhD - Machine Learning-based Surrogate Modeling for Computationally Efficient Multiphysics Simulation(m/w/x)
Renningen
Full-timeWith Home Office
AI/ML
Description
You will redefine engineering boundaries by developing AI-driven surrogate models for multiphysics simulations, creating efficient design protocols that shape the future of industrial components.
Let AI find the perfect jobs for you!
Upload your CV and Nejo AI will find matching job offers for you.
Requirements
- •Master's degree in Engineering, Mathematics, Physics or comparable
- •In-depth knowledge of numerical methods
- •Strong interest or background in machine learning
- •Desirable experience in contact mechanics and EHL
- •Strong programming and scripting experience, preferably Python
- •High motivation and scientific curiosity
- •Independent work on complex issues
- •Clear and concise communication of research
- •Constructive contribution to a team
- •Efficient project organization and overview
- •Fluent written and spoken English
- •Advantageous good German language skills
Education
Master's degree
Tasks
- •Develop scientific foundations for machine learning frameworks
- •Train surrogate models using validated EHL simulations
- •Create data-driven design protocols for lubricated components
- •Accelerate design processes for complex EHL problems
- •Develop robust and reliable tribological components
- •Integrate AI into classical engineering design
- •Apply machine learning to complex engineering challenges
Tools & Technologies
Python
Languages
English – Business Fluent
German – Business Fluent
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bosch Group and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Not a perfect match?
- Bosch Group
PhD - Optimization Of Multi-Scale Simulation Parameters Using Agentic AI(m/w/x)
Full-timeWith HomeofficeNot specifiedRenningen - CARIAD SE
PhD Student - Virtual Simulation Models for LiDAR and Radar Sensors(m/w/x)
Full-timeWith HomeofficeNot specifiedBerlin, Ingolstadt, Wolfsburg, Mönsheim, München - Bosch Group
Mandatory Internship enhance automated CFD workflows for virtual product development with Python(m/w/x)
Full-timeInternshipWith HomeofficeSchwieberdingen - Fraunhofer-Gesellschaft
Studien-/Abschlussarbeit - Architektur zur Anomalie-Lokalisierung: Konzeption und Implementierung(m/w/x)
Full-timeWorking StudentWith HomeofficeStuttgart
BO
Bosch GroupPhD - Machine Learning-based Surrogate Modeling for Computationally Efficient Multiphysics Simulation(m/w/x)
Renningen
Full-timeWith Home Office
AI/ML
New Job?Nejo!
The AI Job Search Engine
Description
You will redefine engineering boundaries by developing AI-driven surrogate models for multiphysics simulations, creating efficient design protocols that shape the future of industrial components.
Let AI find the perfect jobs for you!
Upload your CV and Nejo AI will find matching job offers for you.
Requirements
- •Master's degree in Engineering, Mathematics, Physics or comparable
- •In-depth knowledge of numerical methods
- •Strong interest or background in machine learning
- •Desirable experience in contact mechanics and EHL
- •Strong programming and scripting experience, preferably Python
- •High motivation and scientific curiosity
- •Independent work on complex issues
- •Clear and concise communication of research
- •Constructive contribution to a team
- •Efficient project organization and overview
- •Fluent written and spoken English
- •Advantageous good German language skills
Education
Master's degree
Tasks
- •Develop scientific foundations for machine learning frameworks
- •Train surrogate models using validated EHL simulations
- •Create data-driven design protocols for lubricated components
- •Accelerate design processes for complex EHL problems
- •Develop robust and reliable tribological components
- •Integrate AI into classical engineering design
- •Apply machine learning to complex engineering challenges
Tools & Technologies
Python
Languages
English – Business Fluent
German – Business Fluent
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bosch Group and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
Bosch Group
Industry
Engineering
Description
Das Unternehmen entwickelt hochwertige Technologien und Dienstleistungen, die das Leben der Menschen verbessern.
Not a perfect match?
- Bosch Group
Master Thesis Neural Network-Based Surrogate Models for EHL Contact Simulations(m/w/x)
Full-timeInternshipWith HomeofficeRenningen - Bosch Group
PhD - Optimization Of Multi-Scale Simulation Parameters Using Agentic AI(m/w/x)
Full-timeWith HomeofficeNot specifiedRenningen - CARIAD SE
PhD Student - Virtual Simulation Models for LiDAR and Radar Sensors(m/w/x)
Full-timeWith HomeofficeNot specifiedBerlin, Ingolstadt, Wolfsburg, Mönsheim, München - Bosch Group
Mandatory Internship enhance automated CFD workflows for virtual product development with Python(m/w/x)
Full-timeInternshipWith HomeofficeSchwieberdingen - Fraunhofer-Gesellschaft
Studien-/Abschlussarbeit - Architektur zur Anomalie-Lokalisierung: Konzeption und Implementierung(m/w/x)
Full-timeWorking StudentWith HomeofficeStuttgart