Computer Vision Research Engineer (Image Restoration)

At Huawei Zurich Research Center, we are at the forefront of cutting-edge AI research and development, specializing in creating innovative solutions that push the boundaries of computer vision and deep learning. Our team is a diverse group of passionate researchers, engineers, and creatives dedicated to advancing the state of the art in AI. We are looking for a highly motivated and talented Computer Vision Research Engineer to join our team and lead our efforts in image restoration.

Key Responsibilities:

• Conduct cutting-edge research in computer vision with a focus on image restoration, sensor fusion and time series classification.
• Develop and implement deep learning models and algorithms to aid real-time on-device image restoration tasks.
• Collaborate with cross-functional teams to integrate research outputs into products and services.
• Publish research findings in top-tier conferences and journals.
• Stay up-to-date with the latest advancements in computer vision, deep learning, and related fields.

Qualifications:

• MSc/PhD in Electrical Engineering, Computer Science, or a related field, with a focus on computer vision, deep learning, signal processing, or a similar area.
• Expertise in deep learning frameworks such as PyTorch or similar.
• Extensive experience with time series forecasting, classification, or similar.
• Proficiency in programming languages such as Python, C++, or similar.
• Strong problem-solving skills and ability to work independently as well as in a team environment.
• Excellent communication skills and the ability to present complex technical concepts clearly.

Preferred Qualifications:

• Publication record in top-tier computer vision and AI conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, etc.).
• Experience with image restorations tasks such as super-resolution, denoising, underwater image restoration, flare removal, or similar.
• Experience with real-time processing and optimization of deep learning models for deployment.
• Good understanding of how camera sensors, rolling-shutter and flicker sensors operate.
• Familiarity with multi-modal training, 1D-2D sensor fusion and other advanced machine learning techniques.
• Strong understanding of the theoretical foundations of deep learning and computer vision.