4 дня назад
PhD Student (d/f/m) Intelligent Process Monitoring and Digital Twinning (Cold-Sprayed Repair Applications)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
PhD Student (d/f/m) Intelligent Process Monitoring and Digital Twinning (Cold-Sprayed Repair Applications): Developing a real-time, data-driven monitoring framework for cold-sprayed metallic component repairs with an accent on sensor-based data acquisition, machine learning, AI, and digital twin integration. Focus on detecting process deviations, modeling physical process behavior, building large-scale data pipelines, and validating the monitoring system in an industrial aerospace environment.
Location: Manching, Germany; on-site PhD position with a 36-month duration and a possible research stay at a European partner institution.
Salary: Attractive salary; exact amount not specified.
Company
develops aerospace and defence technologies, including advanced materials and manufacturing processes.
What you will do
- Review sensor-based methods for detecting process irregularities and anomalies in cold-sprayed repairs.
- Identify and integrate sensors for capturing process signals correlated with deposition parameters.
- Build data acquisition and preprocessing pipelines for large-scale datasets.
- Develop machine learning and AI models for recognizing process patterns and quality deviations in real time.
- Create a digital twin framework connecting monitoring data with physical process behavior.
- Validate and demonstrate the complete monitoring pipeline in an industrial setup, including reliability evaluation and scientific publications.
Requirements
- Completed Master’s degree in Computer Science, Mechatronics, Physics, Data Science, engineering, natural sciences, or a related field.
- Strong knowledge of data signal processing and machine learning.
- Programming experience with Python, MATLAB, or C++; familiarity with TensorFlow, PyTorch, or Scikit-learn is desirable.
- Analytical thinking, creative problem-solving, and the ability to model complex physical phenomena.
- Ability to work across interdisciplinary materials, manufacturing, data, and process-monitoring challenges.
- Excellent written and spoken English is required. German is an advantage.
Nice to have
- Basic understanding of manufacturing processes or materials science.
- Experience with ML libraries such as TensorFlow, PyTorch, or Scikit-learn.
Culture & Benefits
- 35-hour working week with flexitime.
- International environment with opportunities for global networking.
- Access to modern and diversified technologies.
- Participation in the Generation Airbus Community.
- Travel within Germany or overseas may be possible after departmental agreement.
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