The Doctoral Candidate will be expected to develop Machine Learning tools that enable automated, objective and efficient identification, classification and quantification (ICQ) and spatial mapping of meso- and macro-scale defects in composite materials, through training on
FIDAMC seeks a Doctoral Candidate to develop machine learning tools for automated identification, classification, and quantification of defects in composite materials, using high- and low-resolution inspection data. Si desea conocer los requisitos para este puesto, siga
The Doctoral Candidate will be expected to develop Machine Learning tools that enable automated, objective and efficient identification, classification and quantification (ICQ) and spatial mapping of meso- and macro-scale defects in composite materials, through training on
The Doctoral Candidate will be expected to develop Machine Learning tools that enable automated, objective and efficient identification, classification and quantification (ICQ) and spatial mapping of meso- and macro-scale defects in composite materials, through training on