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 Haag, Switzerland, with manufacturing sites in Switzerland, Malaysia and Romania, as well as sales and service hubs around the world. We are 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.

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.

Responsibilities

• 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
• 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.

Your Competencies Acquired at the End of the Internship

This internship provides practical experience at the intersection of experimental engineering and data science. By the end of the internship, you will have acquired the following competencies:

• Technical Skills: Hands-on experience with vacuum systems, gas sensing technologies, laboratory experimentation, and semiconductor equipment.
• Data Science Skills: Experience in preprocessing, analyzing, and modelling multichannel time-series sensor data using modern machine learning techniques.
• Experimental Skills: Knowledge of experimental design, systematic data collection, and validation of data-driven methods using laboratory measurements.
• Analytical Skills: Ability to identify patterns in complex datasets, perform feature engineering, and evaluate model performance under varying operating conditions.
• Communication Skills: Experience documenting technical work and presenting results to multidisciplinary stakeholders.
• Collaboration Skills: Practical experience working with researchers and engineers in a dynamic R&D environment.
• Industry Experience: Insight into developing intelligent monitoring and diagnostic solutions for semiconductor manufacturing equipment.

Qualifications

• 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.

Our commitment to inclusion, fairness and equal opportunities

We believe in meaningful careers, continuous learning, and equal opportunities for everyone to drive our innovation and success. We welcome applicants from all backgrounds and are dedicated to fostering an inclusive and respectful workplace. If you have concerns, our compliance channel is available at our website. All reports are handled with strict confidentiality and in accordance with our Code of Conduct.