Internship - Experimental Data Science for Gas Sensing and Time-Series Analytics

VAT Vakuumventile AG; Haag; Vor Ort; CHF 20.000 - 27.000; Teilzeit

Zusammenfassung

VAT Group in Haag, Switzerland, seeks an intern for Experimental Data Science focusing on gas sensing and time-series analytics. You will combine hands-on experiments in a vacuum test laboratory with data science and machine learning to model sensor data during a fixed-term internship.

You will plan experiments, collect multivariate time-series data, preprocess and engineer features, and compare data-driven methods for classification, prediction, monitoring, and anomaly detection.

Qualifikationen

• 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.

Aufgaben

• 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: 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.

Kenntnisse

Python

MATLAB

Data analysis

Statistics

Signal processing

Machine learning

English

Ausbildung

MSc student in data science / related field

Tools

scikit-learn

PyTorch

TensorFlow

MATLAB

Jobbeschreibung

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Internship - Experimental Data Science for Gas Sensing and Time-Series Analytics

Location:

Haag, St. Gallen, CH

Employment Type: Fixed Term Contract

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.

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:
• 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.
• 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.

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 .