Machine Learning (@NAVER AI Lab & CLOVA)

• Main R&R: Publication at top AI venues and contribution to NAVER and AI communities through impactful research

• Position

  • Full-time regular research scientist: OO

  • Research intern (remote work available): OO

• Research topics (Including, but not limited to)

  • Next generation of backbones for image, video, and audio recognition.

    • 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 superresolution for images and videos

    • Neural render and superresolution

  • Hyper-scale language models and their extensions.

    • Controllable LM.

    • Multimodality extension.

    • New evaluation metrics and protocols

    • 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.

    • Automatic speech recognition (ASR).

    • Audio-visual speech recognition.

  • Other topics.

    • Healthcare AI.

    • AI for social good.

• Requirements

  • Research scientist

    • Strong track record of publications at top-tier conferences in machine learning, computer vision, natural language processing, audio, hci, and speech.

    • Relevant job experiences, e.g., laboratory researcher experiences or full-time industrial research experiences.

    • Preferred

      • PhD in CS, EE, mathematics or other related technical fields, or an equivalent work experience.

      • Strong programming skills in Python (PyTorch).

      • Experience with serving as an active member in the research community (e.g. reviewing activities, tutorial and workshop organization, and research code contributions).

    • Responsibilities

      • Organize and execute one’s own research agenda.

      • Lead and collaborate on ambitious research projects.

  • 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.

• Application process:

  • Research scientist:

    • Algorithm coding test (more than one).

    • Tech talk.

    • Job interviews (more than two).

  • Research intern:

    • Algorithm coding test (more than two).

    • Job interviews (more than one).

• Contact: [email protected]