NAVER AI LAB

Introducing NAVER AI LAB Click!

Meet Research Scientists at NAVER AI LAB Click!

[NAVER AI LAB Job Description]

• Research topics
• Including, but not limited to -
• Next generation of backbones for image, video, and audio recognition.
  • Multimodal hyperscale representation
  • Novel neural architecture design (e.g., NAS).
  • Object recognition (e.g., classification, detection, segmentation, retrieval, etc.).
  • Lightweight and energy-efficient models (e.g., pruning, quantization, compression).
  • Learning with large-scale insufficient annotations (e.g., weakly- / self- / semi-supervised learning).
  • Novel learning algorithms for NNs (e.g., normalization, optimization, etc.).
• Generative models for image, video, text, and audio
  • Uncoditional & conditional image generation
  • Image-to-image and vid-to-vid translation
  • Disentanglement and controllability
  • Cross-modal generation
  • Audio and music generation
  • Effective learning algorithm for generative models
  • Style transfer and super-resolution for images and videos
  • Neural render (NeRF) and super-resolution
• Hyperscale language models and their extensions
  • Controllable LM, Hallucination
  • Prompt optimization
  • Multi-modal and Multi-lingual extension.
  • New evaluation metrics
  • Extension to dialogs, QA, summarization, content generation, etc.
• Human computer interaction and interactive AI.
  • Accessibility
  • Computational Interaction
  • Computational Social Science and Social Computing
  • Data-driven Interface Design
  • Human Computation
  • Visualization
• Representation learning for semi-structed or structured data.
  • Graph representation learning
  • Time-series prediction and representation learning
• Trustworthy AI.
  • Explainable AI and causal inference.
  • Robust machine learning (adversarial robustness, domain generalization).
  • De-biased and fair machine learning.
  • Proper uncertainty estimation (e.g. prediction calibration, probabilistic machine learning).
  • Privacy-preserving AI (e.g. differential privacy, federated learning, etc.).
• Audio recognition.
  • Big representation learning for automatic speech recognition (ASR).
  • Audio-visual speech recognition.
• Healthcare AI
  • EMR/EHR based foundation models (large-scale pre-trained language models) for healthcare
  • Clinical predictive modeling with EMR/EHR (e.g., disease prediction, ICD code mapping)
  • Clinical decision support system
  • Medical image analysis for otorhinolaryngology & dentistry
  • Interpretability of AI models (XAI)
  • Causal inference in machine learning & intervention modeling for healthcare services
• Other topics.
  • AI for social good.
  • Reinforcement learning in the wild.
• AI Research with External Collaboration
• SNU-NAVER Hyperscale AI Center
  • Professors: Byung-Gon Chun, Gunhee Kim, Seungwon Hwang, Hyunoh Song, Byoung-Tak Zhang, Taesup Moon, Sang-Goo Lee, Kyomin Jung, Kyoung Mu Lee
  • Main topics (not limited)
  • Advanced hyperscale language models (multimodal, multi-lingual)
  • Reliable and efficient distributed training
  • Overcoming limitations of current hyperscale LMs (hallucination, prompt optimization, bias)
  • Advanced large-scale self-supervised learning
  • Some members will contribute as an adjunct professor of SNU.
• KAIST-NAVER Hypercreative AI Center
  • Professors: Jaegul Choo, Jinwoo Shin, Sung Ju Hwang, Eunho Yang, Jae-Sik Choi, Juho Lee, Kee-Eung Kim, Alice Oh, Juho Kim, Edward Choi, Minjoon Seo
  • Main topics (not limited)
  • Multi- and cross-modal content generation
  • Generation controllability and quality measurement
  • Representation learning for content generation
  • Some members will contribute as an adjunct professor of KAIST.
• Academic Advisors
  • Kyunghyun Cho (NYU): NLP, hyperscale LM
  • Andrew Zisserman (U. of Oxford): Speech recognition and audio-visual representation learning
  • Jun-Yan Zhu (CMU): Generative models
  • Jonghyun Choi (GIST): Continual and online learning
  • Joseph Lim (USC): Reinforcement learning in the wild
• Requirements
• Research intern
  • Experience on research collaborations and paper writing in related fields.
  • Proficient programming skills in Python (PyTorch).
  • Preferred
  • Currently in an MS or PhD programme in CS, EE, mathematics or other related technical fields.
  • Proficient track record of publications at top-tier conferences in machine learning, computer vision, natural language processing, audio, hci, and speech.
• Hiring process:
• Research intern:
  • Algorithm coding test > Paper implementation or tech talk > Job interview

Full publication list