Adlane Ladjal
About Adlane Ladjal
Adlane Ladjal is a Deep Learning and Image Processing Engineer currently employed at Smiths Detection in Vitry-sur-Seine, France. He has a background in computer science and artificial intelligence, holding multiple engineering degrees and experience in developing deep learning models for image analysis.
Work at Smiths Detection
Adlane Ladjal has been employed at Smiths Detection since 2022, serving as a Deep Learning & Image Processing Engineer. His role is based in Vitry-sur-Seine, Île-de-France, France. In this position, he focuses on the application of deep learning techniques to enhance image processing capabilities, particularly in security and detection systems.
Education and Expertise
Adlane Ladjal holds a Diplôme d'ingénieur in Informatique from Université de Sherbrooke, which he completed in 2019. He also earned a Master 2 (M2) in Intelligence artificielle from Université Paul Sabatier Toulouse III, studying from 2020 to 2021. Additionally, he studied Ingénierie informatique at Sup Galilée, obtaining another Diplôme d'ingénieur in 2020. His educational background provides a strong foundation in computer science and artificial intelligence.
Professional Background
Prior to his current role, Adlane Ladjal worked in various positions that contributed to his expertise in data science and engineering. He was a Data Scientist at Sogeti for six months in 2021, a Développeur Python at CACIIS for five months in 2020, and a Data Engineer at Talentsoft for two months in 2019. These roles allowed him to develop skills in data analysis, software development, and machine learning.
Deep Learning Projects
Adlane Ladjal has developed deep learning models specifically aimed at detecting cigarettes and bottles in X-ray scanned images. He also worked on the development of a super-resolution model for X-ray images, enhancing the clarity and detail of scanned images. These projects demonstrate his ability to apply advanced machine learning techniques to practical challenges in image processing.