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 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:
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.
Your Competencies:
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.
Basic knowledge of data analysis, statistics, signal processing, or machine learning.
Programming experience in Python 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.
Good written and spoken English.
Additional experience is a plus:
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.
What you will learn:
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.
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