At Toyota Material Handling Europe (TMHE), we create the technology that keeps the world moving. Diverse businesses across Europe depend on our logistics and material handling solutions. By pioneering technology such as automation, connectivity and productivity solutions, we create opportunities for our customers’ logistics operations to be as efficient as possible. 

We firmly believe in setting ideas in motion. That is why we are investing more than ever in research and development. Our multidisciplinary teams work both conceptually and practically to develop the next generation of material handling products and solutions. By providing our Research and Development organisation with the creative freedom and resources needed to explore new technologies, we aim to stay at the forefront of innovation. At Toyota Material Handling Europe, we contribute to a smarter and more sustainable society, today and tomorrow. 

Background and purpose 
How frequently do we need to collect vehicle data to understand how a forklift is being used? 

Connected forklifts generate large volumes of data through their Controller Area Network (CAN). Collecting data frequently can provide detailed insights into vehicle operation, but it also increases the amount of information that needs to be transmitted and stored. 

At TMHE, we want to understand how different sampling frequencies affect the reliability of usage estimates, using vehicle speed and travelled distance as an initial case. 

The purpose of this thesis is to investigate how reducing the amount of collected data affects estimation accuracy, and whether statistical and machine learning methods can extract reliable information from sparsely sampled vehicle data. The results can provide a foundation for future data-driven applications, such as usage prediction and predictive maintenance. 
 
Thesis description 
Working with recorded vehicle data, you will investigate how reducing the sampling frequency affects the accuracy of travelled-distance estimates. 
Your work will include: 

• Analyse vehicle speed data and establish a method for predicting total travelled distance based on limited samples. 
• Investigate whether statistical methods or machine learning can improve estimates when fewer data points are available. 
• Evaluate how different sampling frequencies influence estimation accuracy. 
• Recommend suitable sampling approaches based on the results and the trade-off between accuracy and data requirements. 

The expected outcome is an evidence-based assessment of how much data is needed to estimate travelled distance reliably, together with recommendations for future data collection. 

We are looking for Master’s students studying: 

• Data Science or Computer Science 
• Machine Learning or a related field 
• Applied Mathematics or Statistics 
• Electrical Engineering or Signal Processing 

You enjoy working with real-world datasets and have experience with programming for data analysis. You have an analytical approach to problem-solving and can evaluate and communicate technical findings clearly in English. 

Beneficial skills (not mandatory) 

• Knowledge of time-series analysis or statistical estimation. 
• Experience with signal processing, data visualisation or machine learning. 
• Familiarity with connected vehicles, telemetry or CAN data. 

At Toyota Material Handling Europe, you will work on a real engineering challenge and explore how machine learning could support future product development decisions. 

You will be based at our IoT Engineering & Connected Solutions department at the Headquarters in Mjölby with possibility to work from our Innovation Centre in Vallastaden, Linköping, where you will receive guidance from experienced professionals and have opportunities to collaborate with engineers and other Master's students. 

We believe in a flexible work environment that allows you to balance both your professional and personal life. At TMHE, you will have the opportunity to build valuable skills, contribute to impactful projects and be part of a collaborative, diverse and welcoming workplace. You will also receive competitive compensation in two instalments: one when you start your work and the other upon successful completion of your thesis. 

Time for you to make a MOVE! 

Your application 
Please submit your application, including: 

• A brief personal letter 
• Your CV 
• A recent transcript of records 

For questions about the thesis, please contact your future mentor, Hanna Häger at hanna.hager@toyota-industries.eu. 

Applications are reviewed continuously and suitable candidates may be invited to interviews before the deadline. 

Terms 

• As soon as possible, or at the latest by the 30th of October 2026. 
• Start date: During January 2027. 
• Scope: 30 hp. 
• Number of students: Two. 
• Location: TMHE Headquarters in Mjölby, with possibility to work from our Innovation Centre in Vallastaden, Linköping.