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Master Thesis Data-Efficient Hybrid Machine Learning for Robust Vibration System Prediction(m/w/x)
Developing data-efficient hybrid ML models for vibration system prediction. Advanced ML techniques and Python proficiency required. Thesis completion with potential for publication.
Anforderungen
- Master's degree in Engineering, Mathematics, Physics, or comparable with good grades
- Good understanding of dynamics (mechanical vibrations) / mechanics
- Very good knowledge of Python (Pytorch, Pandas, Numpy etc.)
- Good to very good knowledge of fundamental machine learning concepts and algorithms, particularly relevant for regression
- High degree of self-motivation
- Independent work
- Effective communication of progress and ideas
- Driving innovation
- Fluent English and basic German
- Fluent German and very good English
- CV, transcript of records, examination regulations attached
- Valid work and residence permit if indicated
Aufgaben
- Investigate developing robust predictive models for technical systems
- Enhance a machine-learning toolbox for vibration-loaded systems
- Add capabilities to learn from scarce measurement data
- Research and apply advanced machine learning techniques
- Integrate limited measurement data into model training
- Develop a benchmark using simulated and new measurement data
- Utilize machine learning algorithms to predict system behavior
- Apply and evaluate chosen machine learning approaches
- Compare model performance against simulation-only models
- Communicate ideas and contributions openly
- Exchange ideas with team colleagues and experts
- Engage with a broader network across company domains and locations
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – fließend
- Deutsch – Grundkenntnisse
Tools & Technologien
- Python
- Pytorch
- Pandas
- Numpy
- Machine learning
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Master Thesis Data-Efficient Hybrid Machine Learning for Robust Vibration System Prediction(m/w/x)
Developing data-efficient hybrid ML models for vibration system prediction. Advanced ML techniques and Python proficiency required. Thesis completion with potential for publication.
Anforderungen
- Master's degree in Engineering, Mathematics, Physics, or comparable with good grades
- Good understanding of dynamics (mechanical vibrations) / mechanics
- Very good knowledge of Python (Pytorch, Pandas, Numpy etc.)
- Good to very good knowledge of fundamental machine learning concepts and algorithms, particularly relevant for regression
- High degree of self-motivation
- Independent work
- Effective communication of progress and ideas
- Driving innovation
- Fluent English and basic German
- Fluent German and very good English
- CV, transcript of records, examination regulations attached
- Valid work and residence permit if indicated
Aufgaben
- Investigate developing robust predictive models for technical systems
- Enhance a machine-learning toolbox for vibration-loaded systems
- Add capabilities to learn from scarce measurement data
- Research and apply advanced machine learning techniques
- Integrate limited measurement data into model training
- Develop a benchmark using simulated and new measurement data
- Utilize machine learning algorithms to predict system behavior
- Apply and evaluate chosen machine learning approaches
- Compare model performance against simulation-only models
- Communicate ideas and contributions openly
- Exchange ideas with team colleagues and experts
- Engage with a broader network across company domains and locations
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – fließend
- Deutsch – Grundkenntnisse
Tools & Technologien
- Python
- Pytorch
- Pandas
- Numpy
- Machine learning
Gefällt dir diese Stelle?
BetaDein Career Agent findet täglich ähnliche Jobs für dich.
Über das Unternehmen
Bosch Group
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Research
Beschreibung
Das Unternehmen entwickelt hochwertige Technologien und Dienstleistungen, die das Leben der Menschen verbessern.
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