Automation and development of algorithms for data analysis of a a galvanometer-based LIBS system.
[Open soon]
Work description
The fellow will join the INESC TEC team within the LIBScan project, carrying out research activities focused on acquisition automation, data processing, and algorithm development for a galvanometer-based LIBS system. The work will focus on the development of automated pipelines for spectral and spatial data pre-processing, as well as on machine learning algorithms, chemical map reconstruction, and algorithmic correction of galvanometric scanning. Training will cover Python programming, spectral signal processing, and hyperspectral data analysis, with emphasis on automated and reproducible data-analysis workflows within the LIBScan project.
Academic Qualifications
Master’s Degree.
Minimum profile required
Master’s Degree in Physics or Physics Engineering.
Preference factors
Experience with spectroscopic techniques (LIBS preferred) Experience with automation Experience in applied optics Python proficiency Experience with Machine learning algorithms
Application Period
Since 05 Feb 2026 to 18 Feb 2026
[Open soon]
Centre
Applied Photonics