Tema

Språk

Global EN
Sverige ENSV
Indien EN

Thesis Worker 30 hp - Integrating Wear and Sensor information

Teknik, data och digitalt · Data, AI och analys · Datavetenskap · Maskininlärning

I korthet

This thesis project focuses on integrating wear and sensor data to predict the Remaining Useful Life (RUL) of components using data-driven methods. The work involves implementing and extending a probabilistic framework, exploring alternative modeling approaches, and evaluating generalization and uncertainty quantification. Ideal for Master's students in Machine Learning, Data Science, or related fields.

Ansvarsområden

  • Reproduce and validate the proposed framework combining survival-based aging and neural-network-based RUL models.
  • Explore alternative modeling approaches for Weibull survival models and/or neural network architectures.
  • Evaluate the framework's generalization across additional datasets and operating conditions.
  • Investigate different approaches for estimating predictive uncertainty for RUL prediction.
  • Potentially investigate extensions to the data-generating formulation for a more general relationship between wear, health, and failure.

Krav

  • Master's programs in Machine Learning, Data Science, Computer Science, Complex Adaptive Systems, or similar.
  • Ability to work at the intersection of machine learning, survival analysis, uncertainty quantification, and predictive maintenance.
  • Proficiency in implementing and evaluating data-driven models.
  • Familiarity with survival-based aging models and neural networks is beneficial.

Önskade kvalifikationer

  • Experience with probabilistic frameworks.
  • Understanding of prediction uncertainty and its effect on distributions, prediction intervals, calibration, and accuracy.

Förmåner

  • An excellent way to get closer to Scania and build relationships for the future.
  • Opportunity to work on a real-world industrial application.

Färdigheter

Machine LearningData ScienceSurvival AnalysisUncertainty QuantificationPredictive MaintenanceNeural Networks
#Thesis Worker#Data Science#Machine Learning#Predictive Maintenance#Scania#TRATON
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Om Scania Group

Scania is a world-leading provider of transport solutions committed to a better tomorrow. Our purpose is to drive the shift towards a sustainable transport system. In doing so, we are creating a world of mobility that’s better for business, society and our environment. Employing more than 50,000 people in about 100 countries, Scania’s research and development is concentrated in Sweden, while production takes place in Europe and South America.

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Företag

Scania Group

Publicerade jobb

för 4 dagar sedan

Anställningstyp

Tillfällig

Arbetsform

På plats

Erfarenhetsnivå

Junior

Platser

Södertälje, Sweden

Kvalifikation

Masterexamen

Sökande

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