Our products are key components in semiconductor processing equipment worldwide. To support the development of intelligent vacuum systems, we are looking for an intern who wants to combine hands-on experimentation with data science and machine learning. During your internship, you will investigate gas dynamics in a vacuum process chamber using advanced sensor technologies and contribute to the complete development cycle, including experimental planning, data collection, preprocessing, feature engineering, time-series modelling, and model validation.
Tasks
- Become familiar with vacuum technology, gas dynamics, process chambers, and sensor systems.
- Support the setup and execution of experiments in the vacuum test laboratory.
- Plan experiments using a structured Design of Experiments approach.
- Collect and organize multivariate time-series data from different sensor technologies.
- Develop data-preprocessing workflows for synchronization, filtering, segmentation, outlier handling, and data-quality assessment.
- Explore the relationships between sensor signals, gas properties, and process conditions.
- Develop and compare data-driven methods.
- Validate the developed methods using independent experiments and different operating conditions.
- Document experimental procedures, datasets, modelling methods, results, and limitations.
- Present findings and potential next steps to engineers and researchers.
Requirements
- MSc student or recent graduate in Data Science, Machine Learning, Physics, Mathematics, Mechanical Engineering, Electrical Engineering, Chemical Engineering, Control Engineering, Mechatronics, or a related field.
- Basic knowledge of data analysis, statistics, signal processing, and machine learning.
- Programming experience in Python, MATLAB, or similar data analysis tools.
- Experience or interest in time-series data analysis, sensor-data analytics, feature engineering, and model validation.
- Interest in combining experimental laboratory work with data-driven modelling.
- Willingness to work hands-on with vacuum equipment, sensors, and data-acquisition systems.
- Experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow is an advantage.
- Knowledge of Design of Experiments (DoE), laboratory measurements, or uncertainty estimation is a plus.
- Strong analytical thinking, independent learning ability, and a collaborative mindset.
- Interest in vacuum technology, gas sensing, process control, or semiconductor manufacturing.
- Good written and spoken English.
Benefits
- Practical experience at the intersection of experimental engineering and data science.
- Hands-on experience with vacuum systems, gas sensing technologies, laboratory experimentation, and semiconductor equipment.
- Experience in preprocessing, analyzing, and modelling multichannel time-series sensor data using modern machine learning techniques.
- Knowledge of experimental design, systematic data collection, and validation of data-driven methods using laboratory measurements.
- Ability to identify patterns in complex datasets, perform feature engineering, and evaluate model performance under varying operating conditions.
- Experience documenting technical work and presenting results to multidisciplinary stakeholders.
- Practical experience working with researchers and engineers in a dynamic R&D environment.
- Insight into developing intelligent monitoring and diagnostic solutions for semiconductor manufacturing equipment.
- Commitment to inclusion, fairness, and equal opportunities.
- Support for continuous learning and career growth.
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Über uns
Das Team
Joining VAT means becoming part of a passionate, international team where your voice is heard, your ideas matter, and your career growth is supported. You will collaborate with researchers and engineers in a dynamic R&D environment.