Position Overview
As a Computer Vision Data Engineer, you will be responsible for developing and optimizing high-precision, automated semiconductor metrology and process result analysis algorithms. You will drive the application of advanced vision technology in semiconductor production processes, including vision-based wafer transfer mechanism correction and alignment, and chamber state analysis, etc. Additionally, you will participate in cutting-edge visual analysis projects in the medical and life sciences fields, such as the identification and analysis of biological cells, fostering cross-disciplinary technological innovation.
Key Responsibilities
Algorithm Development: Design and optimize high-precision, automated computer vision algorithms for semiconductor metrology, process result analysis, mechanical correction, and alignment, ensuring robustness and efficiency.
Full Project Lifecycle Participation: Engage in all phases of computer vision projects, including requirement analysis, data collection and processing, model training and optimization, algorithm validation and deployment, ensuring timely and high-quality project delivery.
Cross-Disciplinary Collaboration: Participate in visual analysis projects in the medical and life sciences fields, collaborating with biomedical engineering experts to provide cutting-edge computer vision solutions and drive project success.
Research and Innovation: Track and integrate the latest research findings in computer vision and deep learning fields, driving continuous technological advancements.
Main Requirements
Educational Background: Ph.D. / Master in Computer Science, Electronic Engineering, Automation, Computer Engineering, or related fields.
Computer Vision Skills: In-depth understanding of image processing principles and techniques, proficient in edge analysis, morphological analysis, texture analysis, and the application of deep learning models for segmentation, object detection, and image classification, etc.
Data Processing and Feature Engineering: Expertise in data processing and feature engineering, including feature selection, construction, and fusion methods, capable of handling large-scale data and extracting valuable information.
Programming Skills: Proficiency in at least one of the following languages: Python, C/C++, Halcon, MATLAB. Familiarity with scientific computing libraries such as OpenCV, Scikit-Image, Albumentations, Pillow, Sklearn. Deep understanding of deep learning frameworks such as Pytorch, Tensorflow, Keras, with experience in complex system development and optimization.
Communication Skills: Excellent oral and written communication skills, with the ability to write technical documentation and academic papers in fluent English.
Industry Experience: Experience in the semiconductor, medical, or life sciences fields. Preferably published high-level papers in computer vision or data science conferences or journals.