- Conduct original research on continual, interactive, and lifelong learning for multimodal AI systems.
- Develop methods that enable AI systems to adapt, personalize, and improve through interaction with human users.
- Investigate mechanisms for human-AI interactive learning, including lightweight adaptation, memory and retrieval, uncertainty-aware learning, and learning from human feedback.
- Develop algorithms that remain robust under distribution shifts, changing user populations, evolving tasks, and changing multimodal inputs.
- Advance methods for user modeling, personalization, and human-AI co-adaptation through repeated interaction.
- Design and utilize simulated and real-world environments to study adaptation, personalization, and human-AI collaboration.
- Design rigorous evaluation methodologies and benchmarks to measure adaptation, robustness, and long-term human-AI interaction.
- Conduct empirical analyses of adaptation dynamics, including sample efficiency, calibration, stability, and catastrophic forgetting.
- Collaborate with interdisciplinary teams and contribute to publications, patents, prototypes, and research innovations.
Minimum Qualifications
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- Ph.D. in Computer Science, Robotics, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related field.
- Strong research record in machine learning, artificial intelligence, robotics, or related disciplines, demonstrated through publications, impactful projects, or equivalent research contributions.
- Demonstrated ability to formulate, lead, and execute independent research programs.
- Deep expertise in one or more of the following areas:
- Continual learning, online learning, test-time adaptation, personalization, or learning from human feedback.
- Multimodal AI systems, including representation learning, alignment, grounding, or reasoning across modalities.
- Human-AI interaction, human-aware AI, assistive AI, or interactive learning systems.
- Embodied AI, decision-making systems, or adaptive AI systems operating in real time.
- Strong communication, presentation, and collaboration skills.
- 1 - 3 years of relevant work experience.
Bonus Qualifications
- Experience with continual learning, online learning, test-time adaptation, memory-augmented systems, retrieval-augmented systems, or learning from human feedback.
- Experience with personalization, user modeling, adaptive assistants, or long-term human-AI interaction.
- Experience with uncertainty estimation, calibration, and robust adaptation in multimodal systems.
- Experience with multimodal reasoning, agentic systems, planning, or decision-making in collaborative human-AI environments.
- Experience with simulation and benchmarking platforms for embodied AI, human-AI interaction, or multi-agent systems.
- Publication record at leading venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, AAAI, RSS, or CoRL.
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| Desired Start Date |
7/6/2026 |
| Position Keywords |
Machine Learning, Continual Learning, multimodal AI, human-AI co-adaptation |
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