Computer Vision Scientist

Huawei Amsterdam Research Center is at the forefront of artificial intelligence and machine learning innovation. We are dedicated to developing cutting-edge technologies that revolutionize how people interact with information and enhance productivity across various sectors. Our team of experts is committed to pushing the boundaries of what's possible in AI, and we are looking for talented individuals to join us on this exciting journey.

Role Overview

As a Research Engineer in Computer Vision, you will apply advanced deep learning and multimodal modeling techniques to improve how AI systems perceive and interpret the world. You’ll work on challenges such as visual recognition, representation learning, and scene or behavior understanding, with opportunities to explore emotion or intent recognition when relevant. Your contributions will help bridge the gap between research prototypes and deployable intelligent systems.

Key Responsibilities

• Conduct research in computer vision and multimodal learning models for large-scale visual understanding tasks.
• Translate research into practice by prototyping, evaluating, and improving deployable AI systems.
• Collaborate with interdisciplinary teams across AI, cognitive science, and human-computer interaction (HCI).
• Publish in top-tier conferences and journals (e.g., CVPR, ICCV, NeurIPS, ICML, ICLR, AAAI, or similar).
  
  
  

Qualifications

• PhD. or Master’s degree in Computer Science, Artificial Intelligence, Computer Vision, or a related field.
• Strong background in deep learning and Python, with hands-on experience using frameworks such as PyTorch.
• Solid experience in computer vision, representation learning, or multimodal fusion.
• Excellent analytical and problem-solving skills; ability to work both independently and collaboratively.
  
  
  

Preferred Qualifications

• Demonstrated research excellence through publications in top-tier venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AAAI, IJCAI).
• Experience with multimodal large models, including visual-language, and audio-language models.
• Familiarity with emotion recognition and sentiment analysis.
• Awareness of HCI principles and ethical considerations in emotion-aware AI.
• Track record of interdisciplinary collaboration or applied research.