Master thesis - Understanding Retarder Stalk Usage Through Data Analytics and Machine Learning
Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning
In short
Zwei Master-Studenten werden gesucht, um Datenanalysen und maschinelles Lernen auf reale Fahrzeugdaten anzuwenden, um das Nutzungsverhalten von Fahrerassistenzsystemen in Schwerlastkraftwagen zu verstehen und zu verbessern. Die Arbeit konzentriert sich auf die Analyse von Daten zur Fahrerinteraktion, zur Identifizierung von Mustern und zur Untersuchung von Zusammenhängen mit Fahrbedingungen und Bremsverhalten.
Responsibilities
- Analyze real-world vehicle data to characterize retarder stalk usage across driving sessions.
- Identify common usage patterns in stalk position distributions, transition sequences, dwell times, and switching frequency (e.g. Markov chain models).
- Discover driver usage profiles, such as stable usage, frequent changes, and short-term repeated transitions, using clustering methods (e.g. GMM, HDBSCAN) and latent-state models (e.g. HMM).
- Investigate the relationship between stalk position changes and braking behaviour before, during, and after transitions.
- Assess how driving conditions, such as vehicle speed, road gradient, and vehicle load, influence observed usage patterns.
Requirements
- Currently pursuing a Master’s degree in Data Science, Computer Science or Computer Engineering, Electrical Engineering, or Applied Mathematics.
- Strong interest in Data analytics and machine learning.
- Strong interest in Time-series analysis, clustering, and behavioral pattern recognition.
- Strong interest in Vehicle systems and automotive engineering.
- Strong interest in Statistical analysis, visualization, and data-driven problem-solving.
Desired Qualifications
- Experience with Python, large-scale datasets, or machine learning is considered an advantage.
- Familiarity with vehicle data or signal analysis is a plus.
Benefits
- Hands-on experience applying data analytics and machine learning to real-world, high-frequency vehicle data.
- Insight into how data science supports heavy-duty vehicle development in an industrial setting.
Skills
Data AnalyticsMachine LearningTime-series analysisClusteringBehavioral pattern recognitionStatistical analysisData visualizationPythonMarkov chain modelsGMMHDBSCANHMMSignal analysis
#Data Analytics#Machine Learning#Time-series analysis#Clustering#Behavioral pattern recognition#Statistical analysis#Data visualization#Python#Markov chain models#GMM#HDBSCAN#HMM#Signal analysis#Volvo Group#Automotive#Heavy-duty trucks#Sustainable transport#Master Thesis
About Volvo Group
The Volvo Group is one of the world’s leading manufacturers of trucks, buses, construction equipment and marine and industrial engines. The Group also provides complete solutions for financing and service. The Volvo Group, with its headquarters in Gothenburg, employs about 100,000 people, has production facilities in 18 countries and sells its products in more than 190 markets.\n\n
Company
Volvo GroupJob Posted
3 days ago
Expires
in 1 week
Employment Type
Temporary
Work mode
On Site
Experience Level
Student
Locations
Göteborg, Sweden
Qualification
Master
Applicants
Be an early applicant
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