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Internship - Experimental Data Science for Gas Sensing and Time-Series Analytics
About VAT Group At VAT, we change the world with vacuum solutions. As the world's leading supplier of high-performance vacuum valves, we have been driving innovation for more than 60 years. With over 3,200 employees worldwide, we operate from our headquarters in , Switzerland, with manufacturing sites in Switzerland, Malaysia and Romania, as well as sales and service hubs around the world. We guided by our passions integrity, teamwork, customer centricity, and innovation - always working together as #oneVAT. Joining us means becoming part of a passionate, international team where your voice is heard, your ideas matter, and your career growth is supported.
Internship - Experimental Data Science for Gas Sensing and Time-Series Analytics
Your Challenge:
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. You will contribute to the complete development cycle, including experimental planning, data collection, preprocessing, feature engineering, time-series modelling, and model validation.
Your responsibilities will include:
Your Competencies:
Additional experience is a plus:
What you will learn:
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- Become familiar with vacuum technology, gas dynamics, process chambers, and sensor systems.
- Support the setup and execution of experiments in our 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 for:
- Feature extraction
- Gas or process-condition classification
- Prediction and regression
- Process monitoring
- Event or anomaly detection
- Validate the developed methods using independent experiments and different operating conditions.
- Document experimental procedures, datasets, modelling methods, results, and limitations.
- Present your findings and potential next steps to engineers and researchers.
- Currently pursuing an MSc degree in data science, machine learning, physics, mathematics, mechanical engineering, electrical engineering, chemical engineering, control engineering, mechatronics, or a related field.
- knowledge of data analysis, statistics, signal processing, or machine learning.
- Programming experience in or MATLAB.
- Interest in combining experimental work with data-driven modelling.
- Willingness to work hands-on with vacuum equipment, sensors, and data-acquisition systems.
- Systematic thinking and enthusiasm for developing innovative technical solutions.
- Ability to learn independently and collaborate in a multidisciplinary R&D environment.
- written and spoken English.
- Experience with time-series data, signal processing, or sensor-data analytics.
- Familiarity with scikit-learn, PyTorch, TensorFlow, MATLAB, or similar tools.
- Experience with Design of Experiments or laboratory measurements.
- Knowledge of feature engineering, model validation, or uncertainty estimation.
- Interest in vacuum technology, gas sensing, process control, or semiconductor manufacturing.
- Hands-on experience with vacuum systems and advanced sensor technologies.
- Experimental design and systematic data collection.
- Preprocessing and analysis of large multichannel sensor datasets.
- Time-series machine learning for real physical systems.
- Validation of data-driven models through laboratory experiments.
- Application knowledge in gas sensing and semiconductor equipment.
