Real-Time Grain-Size AI from Laser Ultrasonics

Swerim AB; Stockholms kommun; On-site; SEK 45,000 - 55,000; Full time

Job summary

Swerim AB in Stockholm invites you to join the NAICE project as a master thesis student in Laser Ultrasonics data science. You will apply machine learning to LUS data from hot strip mill conditions, focusing on grain size prediction and microstructure classification.

The work involves pre-processing high-resolution A-scan data, surveying AI architectures, developing models, and benchmarking performance for real-time applicability.

Qualifications

• A background in computer engineering, engineering physics, materials science, applied mathematics, or similar.
• Strong interest in machine learning and signal processing.
• Experience with Python/Matlab and ML frameworks.
• Basic understanding of wave physics, ultrasonics, or materials science.
• Analytical mindset and ability to work with complex datasets.

Responsibilities

• Survey AI/ML architectures for ultrasonic time-series data.
• Pre-process and structure high-resolution LUS datasets for model training.
• Develop and implement selected AI models.
• Perform benchmark comparisons on accuracy, robustness, and efficiency.
• Analyze model interpretability in relation to physical phenomena.

Skills

Python

Matlab

ML frameworks

Signal processing

Analytical mindset

Education

Master's student in related field

Tools

Python

MATLAB

Job description

Swerim AB in Stockholm invites you to join the NAICE project as a master thesis student in Laser Ultrasonics data science. You will apply machine learning to LUS data from hot strip mill conditions, focusing on grain size prediction and microstructure classification.

The work involves pre-processing high-resolution A-scan data, surveying AI architectures, developing models, and benchmarking performance for real-time applicability.