NAVER AI LAB
NAVER AI LAB aims to achieve results that will surprise the world through impactful research, thus contributing to the global AI research community, as the mid&long-term AI research team of NAVER.
[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
- 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
Contact : [email protected]
Last modified 12d ago