Thesis Work: AI & Machine Learning for Cost and Carbon Footprint Estimation of Electronic Components

About this opportunity - Background

We are seeking two motivated master’s students to explore and develop advanced AI and machine learning methods for estimating both the cost and carbon footprint of electronic components.

Scope of the thesis work

• Data Analysis & Feature Engineering: Investigate and preprocess data from electronic Bills of Materials (BoMs), supplier reports, and historical component databases. Incorporate technical parameters of components as input features to improve model accuracy.
• Algorithm Development: Design, implement, and evaluate machine learning models for predicting component cost and carbon footprint, leveraging both numerical data and external market/industry reports.
• Historical Data Modeling: Use AI/ML techniques to analyze historical pricing and carbon data to estimate future high-volume pricing trends.
• Component Categorization: Apply AI techniques for automated classification and clustering of electronic components, enabling an efficient and standardized workflow.
• Validation & Benchmarking: Compare developed models against traditional estimation methods and validate and train models using existing data.
• Tool Integration & Automation: Propose approaches for integrating the developed algorithms into existing cost engineering workflows, with a focus on automating repetitive tasks, minimizing manual effort and eliminating redundant tasks, and enabling efficient reporting.
• Role Evolution Study: As part of the thesis, students will also analyze how cost engineers work today—tools, processes, and estimation principles—and assess how AI and ML will reshape their role in the future. This includes identifying opportunities for human-AI collaboration, shifting skill requirements, and the potential for more strategic, insight-driven responsibilities.

Expected Outcomes:

• Prototypes of AI/ML models for cost and carbon footprint estimation
• Documentation of methodology, results, and recommendations for industrial   implementation
• Insights into the evolving role of cost engineers in an AI-driven environment

What you'll bring

• Background in computer science, data science, electrical engineering, or similar
• Interest in AI/ML, sustainability, and electronics manufacturing
• Experience with Python and relevant ML libraries is a plus
• Strong analytical skills and the ability to work independently are desirable

The thesis will be conducted on site at Cost Engineering Electrical/Software at Volvo Cars Gothenburg.

Duration

•    The work will start in January 2026
•    The duration for this thesis work is 20 weeks
•    30 ECTS in agreement with your Thesis Advisor at the University
•    This thesis is to be conducted by 2 Students working in pair