ABB AG
Internship - Learning-Based Optimal Control for Industrial Applications(m/w/x)
Full-timeInternshipOn-site
Mannheim
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ABABB AG
Internship for Optimization of Industrial Asset Management(m/w/x)
Mannheim
Full-timeInternshipOn-site
Nejo AI Summary
Developing mathematical models and multi-stage optimization algorithms for critical industrial assets in energy-intensive industries. Master's degree in STEM and publication experience required. Direct contribution to global industrial efficiency.
Requirements
- Master's degree in Electrical Engineering, Industrial Engineering, Systems Engineering, Computer Science, Mathematics, or similar field
- Modeling complex systems from first-principles equations
- Familiarity with mathematical optimization and algorithms
- Programming experience in Python or C/C++
- Publication experience
- Familiarity with Git and LaTeX
- Proficiency in using optimization solvers like CPLEX, Gurobi, or open-source solvers
- Good communication skills in English
Tasks
- Contribute to research on optimizing Operations & Maintenance for energy-intensive industries
- Develop mathematical models for lifecycle management of critical industrial assets
- Implement multi-stage optimization algorithms to balance strategic goals with operational decisions
- Analyze and summarize results, preparing technical documentation
- Demonstrate readiness to tackle complex problems and implement developed models
- Drive independent work tasks as a project team member
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- C/C++
- Git
- LaTeX
- CPLEX
- Gurobi
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of ABB AG 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.
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ABABB AG
Internship for Optimization of Industrial Asset Management(m/w/x)
Mannheim
Full-timeInternshipOn-site
Nejo AI Summary
Developing mathematical models and multi-stage optimization algorithms for critical industrial assets in energy-intensive industries. Master's degree in STEM and publication experience required. Direct contribution to global industrial efficiency.
Requirements
- Master's degree in Electrical Engineering, Industrial Engineering, Systems Engineering, Computer Science, Mathematics, or similar field
- Modeling complex systems from first-principles equations
- Familiarity with mathematical optimization and algorithms
- Programming experience in Python or C/C++
- Publication experience
- Familiarity with Git and LaTeX
- Proficiency in using optimization solvers like CPLEX, Gurobi, or open-source solvers
- Good communication skills in English
Tasks
- Contribute to research on optimizing Operations & Maintenance for energy-intensive industries
- Develop mathematical models for lifecycle management of critical industrial assets
- Implement multi-stage optimization algorithms to balance strategic goals with operational decisions
- Analyze and summarize results, preparing technical documentation
- Demonstrate readiness to tackle complex problems and implement developed models
- Drive independent work tasks as a project team member
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- C/C++
- Git
- LaTeX
- CPLEX
- Gurobi
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of ABB AG 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
ABB AG
Industry
Engineering
Description
Das Unternehmen ist ein globaler Marktführer, der Industrien dabei unterstützt, effizienter und umweltfreundlicher zu arbeiten.
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