ABB AG
Internship/Master Thesis student - Highly Dynamic Control of Electric Drives(m/w/x)
Full-timeInternshipOn-site
Mannheim
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ABABB AG
Internship / Master Thesis - Physics-Informed AI on Complex Thermal Processes(m/w/x)
Mannheim
Full-timeInternshipOn-site
AI/ML
Data Science
Nejo AI Summary
Researching physics-informed AI for complex thermal processes at ABB. Python and data analytics libraries knowledge required. Hybrid work, 6 weeks vacation.
Requirements
- Interest in machine learning and conventional systems
- Currently enrolled in applied mathematics, physics, computer science, data science, engineering, or similar knowledge from software development
- Sound knowledge in Python and shell scripting
- Good knowledge in data analytics libraries (Tensorflow, Scikit-Learn, Numpy, etc.)
- Self-motivation and ability to work independently
- Fluent English writing and speaking skills
- Familiarity with Linux systems
Tasks
- Conduct research in physics-informed AI
- Develop advanced physics-informed AI models
- Identify key features of process stability
- Perform data analytics
- Apply physics-informed machine learning
- Develop Python packages for industrial use
- Collaborate closely with scientists and engineers
- Develop physics-informed machine learning approaches
- Validate machine learning approaches
- Develop demonstrators and software prototypes
- Propose new concepts and ideas
- Overcome potential challenges
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Python
- shell scripting
- Tensorflow
- Scikit-Learn
- Numpy
- Linux
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 / Master Thesis - Physics-Informed AI on Complex Thermal Processes(m/w/x)
Mannheim
Full-timeInternshipOn-site
AI/ML
Data Science
Nejo AI Summary
Researching physics-informed AI for complex thermal processes at ABB. Python and data analytics libraries knowledge required. Hybrid work, 6 weeks vacation.
Requirements
- Interest in machine learning and conventional systems
- Currently enrolled in applied mathematics, physics, computer science, data science, engineering, or similar knowledge from software development
- Sound knowledge in Python and shell scripting
- Good knowledge in data analytics libraries (Tensorflow, Scikit-Learn, Numpy, etc.)
- Self-motivation and ability to work independently
- Fluent English writing and speaking skills
- Familiarity with Linux systems
Tasks
- Conduct research in physics-informed AI
- Develop advanced physics-informed AI models
- Identify key features of process stability
- Perform data analytics
- Apply physics-informed machine learning
- Develop Python packages for industrial use
- Collaborate closely with scientists and engineers
- Develop physics-informed machine learning approaches
- Validate machine learning approaches
- Develop demonstrators and software prototypes
- Propose new concepts and ideas
- Overcome potential challenges
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Python
- shell scripting
- Tensorflow
- Scikit-Learn
- Numpy
- Linux
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.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
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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