Qureos Mentor

Honda Research Institute USA

<div><div><div>Job Number: P25INT-48
</div><div>Honda Research Institute USA (HRI-US) is seeking a self-motivated intern to work on foundational research focusing at the intersection of test-time adaptivity for AI agents and human-AI collaboration. The intern will work on building and evaluating adaptive, agentic AI systems that can reason, learn, and adjust their behavior in response to changing environments and human feedback. The project explores how multimodal AI agents operating can collaborate with humans more effectively through adaptive inference, interactive decision-making, and goal-directed behavior. This role is ideal for a student interested in modern AI research spanning foundation models, embodied AI, reasoning systems, and human-centered AI.
</div><div>San Jose, CA</div><br>
<p></p><div><div><div><div><div><p><b>Key Responsibilities</b></p><br>
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<p></p><div><div><ul><li>Design, implement, and evaluate adaptive AI agents that operate under uncertainty and adjust behavior at test time based on context, feedback, or human interaction.</li>
<li>Conduct research on human&ndash;AI collaboration, including interaction protocols, shared autonomy, and mechanisms for incorporating human guidance into agent decision-making.</li>
<li>Work with simulation environments (e.g., VirtualHome, Habitat, or related embodied AI platforms) to build tasks, scenarios, and evaluation frameworks.</li>
<li>Develop and experiment with multimodal models and agents, integrating vision, language, and action for embodied or interactive settings.</li>
<li>Investigate reasoning and agentic behaviors, such as planning, decomposition, memory, tool use, or self-reflection in adaptive systems.</li>
<li>Run controlled experiments, analyze results, and iterate on model and system design based on empirical findings.</li>
<li>Document methodologies and findings through technical reports, presentations, and research papers.</li>
<li>Collaborate with cross-disciplinary team members and participate in research discussions and design reviews.</li><br>
</ul><p></p><p><b>Minimum Qualifications</b></p><br>
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<p></p><div><div><ul><li>Currently enrolled as a M.S. or a Ph.D. student in Computer Science, Machine Learning, or a related field at a reputed university.</li>
<li>Research experience in projects related to reinforcement learning, meta learning, transfer learning, lifelong learning.</li>
<li>Experience in open-source deep learning frameworks.</li><br>
</ul><p></p><p><b>Bonus Qualifications</b>
</p><div><div><div><div><ul><li>Experience with multi-agents settings (simulation testbeds, etc).</li>
<li>Familiarity with post-training multimodal AI models.</li>
<li>Strong publication records in topics related to multi-agent systems in robotics or AI conferences (RSS, CORL, NeurIPS, AAAI, AAMAS, ICLR).</li><br>
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</div></div><p></p><div><div><div><div><div><div><div><div><div><div><div><div><b>Years of Work Experience Required</b></div> <div>0</div>
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<div><div><b>Desired Start Date</b></div> <div>8/31/2026</div>
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<div><div><b>Internship Duration</b></div> <div>3 Months</div>
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<div><div><b>Position Keywords</b></div> <div>Adaptivity, Agentic AI, Human-AI Collaboration</div>
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Research Intern: Adaptivity For Autonomous Agents

<div><div><div>Job Number: P25INT-42
</div><div>Honda Research Institute USA (HRI-US) is seeking a self-motivated and independent intern to join our research team working on multimodal machine learning for egocentric perception and skill understanding. This role will focus on developing learning methods that align hand pose information with language representations, enabling fine-grained hand motion and pose cues to be more effectively incorporated into modern downstream models, including large language model-based systems. The intern will work with egocentric video and hand pose data, including data captured from wearable devices such as Meta Aria glasses, to build representations that support tasks such as error detection, temporal action segmentation, long-horizon video understanding, and skill assessment. This is a research-oriented internship with the goal of producing high-impact results suitable for publication at a top-tier conference.
</div><div>San Jose, CA</div><br>
<p></p><div><div><div><div><div><p><b>Key Responsibilities</b></p><br>
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<p></p><div><div><ul><li>Conduct literature reviews to identify relevant prior work, formulate research hypotheses, and propose promising technical directions in hand pose-language representation learning.</li>
<li>Design and implement machine learning methods for aligning hand pose, egocentric video, and language using large-scale multimodal datasets.</li>
<li>Develop or adapt hand pose encoders that learn semantically rich representations for downstream multimodal reasoning and language-conditioned tasks.</li>
<li>Investigate pretraining strategies that align structured hand motion and pose information with natural language, analogous to image-language pretraining in vision-language models.</li>
<li>Design rigorous experiments, evaluation protocols, and ablation studies to assess representation quality and downstream task performance.</li>
<li>Apply and evaluate learned representations on downstream tasks such as error detection, temporal action segmentation, action detection, long (procedural) video understanding, skill estimation, and dexterity assessment.</li>
<li>Build data processing, training, and evaluation pipelines for multimodal learning with hand pose, egocentric video, and text.</li>
<li>Analyze experimental results, synthesize technical insights, and communicate findings through internal presentations, technical reports, and research publications.</li>
<li>Collaborate closely with researchers to refine problem formulations, iterate on model designs, and identify high-impact publication opportunities (e.g., CVPR, ICCV, ICLR, ECCV, NeurIPS).</li><br>
</ul><p></p><p><b>Minimum Qualifications</b></p><br>
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<p></p><div><div><ul><li>Currently pursuing a Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field.</li>
<li>Strong background in machine learning and deep learning, with hands-on experience training neural networks for representation learning or multimodal learning.</li>
<li>Proficiency in Python and deep learning frameworks such as PyTorch.</li>
<li>Demonstrated understanding of one or more of the following areas: computer vision, sequence modeling, multimodal learning, human pose modeling, action recognition, or large language models.</li>
<li>Experience designing and running empirical ML experiments, including data preprocessing, model training, evaluation, ablation studies, and model fine-tuning.</li>
<li>Familiarity with fine-tuning strategies for deep learning or foundation models in research settings.</li>
<li>Strong written and verbal communication skills, with the ability to read and implement ideas from recent research papers.</li><br>
</ul><p></p><p><b>Bonus Qualifications</b>
</p><div><div><div><div><ul><li>Research experience in egocentric video understanding, human/hand pose estimation, action recognition, temporal action segmentation, or vision-language learning.</li>
<li>Experience with video understanding tasks and human activity datasets, especially in the context of action recognition and/or action segmentation.</li>
<li>Experience working with structured motion signals such as 2D/3D key points, skeletal data, trajectories, or sensor-based representations.</li>
<li>Experience with large-scale pretraining of vision-language models (VLMs), large language models (LLMs), or fine-tuning of foundation models.</li>
<li>Familiarity with efficient fine-tuning techniques such as LoRA and adapters.</li>
<li>Familiarity with foundation models or methods for integrating non-visual modalities into language-driven systems.</li>
<li>Experience with multimodal alignment across modalities such as video, pose, and language.</li>
<li>Familiarity with 3D vision or SLAM, particularly for modeling hand or human motion in a world coordinate frame.</li>
<li>Experience with large-scale training, distributed experimentation, or multimodal dataset curation.</li>
<li>Track record of research publications in leading venues such as CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, ACL, or EMNLP.</li>
Interest in applying AI to human skill modeling, worker assistance, and human-centered intelligent systems.<br>
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</div></div><p></p><div><div><div><div><div><div><div><div><div><div><div><div><b>Years of Work Experience Required</b></div> <div>0</div>
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<div><div><b>Desired Start Date</b></div> <div>9/8/2026</div>
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<div><div><b>Internship Duration</b></div> <div>3 Months</div>
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<div><div><b>Position Keywords</b></div>
 <div><p>Multimodal Learning, Hand Pose Modeling, Egocentric Video Understanding,</p>
 <p>Pose-Language Alignment, Representation Learning, Large Language Models</p>
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Research Intern: Hand Pose-Language Representation Learning

<div><div><div>Job Number: P25INT-43
</div><div>Honda Research Institute USA (HRI-US) is seeking a highly motivated intern to join its intelligent autonomy and AI research efforts. This role focuses on advancing approaches for handling rare, long-tail scenarios in autonomous driving by exploring complementary modeling paradigms. The candidate will work with modern multimodal and predictive modeling techniques, including vision-language(-action) models and world modeling approaches, to better understand and represent complex real-world situations. The work will contribute to improving the robustness, interpretability, and reliability of intelligent autonomous systems.
</div><div>San Jose, CA</div><br>
<p></p><div><div><div><div><div><p><b>Key Responsibilities</b></p><br>
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<p></p><div><div><ul><li>Develop multimodal and predictive models, including vision-language(-action) and world models, using post-training (e.g. fine-tuning) to improve performance in rare, safety-critical scenarios.</li>
<li>Curate and preprocess datasets from public benchmarks with a focus on long-tail and edge-case conditions.</li>
<li>Design experiments to evaluate model behavior in complex scenarios and analyze results to identify strengths, limitations, failure modes, and potential improvements in rare-event settings.
</li><li>Collaborate with cross-functional teams to align research direction and technical goals.</li>
<li>Contribute to a portfolio of patents, academic publications, and prototypes to demonstrate research value.</li><br>
</ul><p></p><p><b>Minimum Qualifications</b></p><br>
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<p></p><div><div><ul><li>M.S. in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, Machine Learning, or a related field.</li>
<li>Strong background in machine learning, deep learning, or multimodal AI, including experience with vision-language(-action) models and/or world models.</li>
<li>Experience with model training, fine-tuning, or large-scale data processing.</li>
<li>Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow).</li>
<li>Strong written and verbal communication skills, with the ability to present technical ideas and results clearly to diverse audiences.</li><br>
</ul><p></p><p><b>Bonus Qualifications</b>
</p><div><div><div><div><ul><li>Ph.D. in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, Machine Learning, or a related field.</li>
<li>Familiarity with autonomous systems, robotics, or mobility-related datasets.</li>
<li>Experience with parameter-efficient training methods (e.g., LoRA, adapters).</li>
<li>Exposure to long-tail/edge-case analysis or safety-critical systems.</li>
<li>Strong analytical and problem-solving skills for diagnosing model behavior.</li>
<li>Publication record in top-tier conferences (e.g., CVPR, ICCV, ECCV, WACV, NeurIPS, ICLR).</li><br>
</ul></div></div><br>
</div></div><p></p><div><div><div><div><div><div><div><div><div><div><div><div><b>Years of Work Experience Required</b></div> <div>0</div>
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<div><div><b>Desired Start Date</b></div> <div>8/31/2026</div>
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<div><div><b>Internship Duration</b></div> <div>3 Months</div>
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<div><div><b>Position Keywords</b></div> <div>Mutimodal learning, vision-language(-action) models, world models, long-tail scenarios, autonomous driving</div>
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Research Intern: Vision-Language-Action and World Models for Autonomous Systems

<div><div><div>Job Number: P25INT-45
</div><div>Honda Research Institute USA (HRI-US) is seeking a computational social scientist intern with experience in modeling human and community behavior using AI, and in studying the impacts of AI on society. The successful candidate will contribute to research by developing AI models of individuals and communities that simulate complex behaviors across modalities, supported by rich community-level data such as mobility trajectories, census data, and survey responses. The candidate will contribute to the development and evaluation of computational and machine learning models, analyze complex behavioral and mobility datasets, and apply societal-scale simulations to inform decision-making in domains such as technology rollout. The position will also entail conducting impact analyses from the individual to the community level using robust counterfactual evaluation methods.
</div><div>San Jose, CA</div><br>
<p></p><div><div><div><div><div><p><b>Key Responsibilities</b></p><br>
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<p></p><div><div><ul><li>Develop and evaluate machine learning and computational modeling approaches for studying communities, social behavior, and mobility patterns.</li>
<li>Analyze multimodal behavioral data and community mobility data using statistical and causal inference methods.</li>
<li>Apply counterfactual explanation methods to support interpretation of model outputs and research findings.</li>
<li>Contribute to research on the societal impacts of generative AI.</li>
<li>Assist with brainstorming and refining research ideas related to ongoing project goals.</li>
<li>Lead the preparation of manuscripts, figures, analyses, and other research outputs for publication.</li><br>
</ul><p></p><p><b>Minimum Qualifications</b></p><br>
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<p></p><div><div><ul><li>Currently enrolled as a Ph.D. candidate in Computer Science or a related field.</li>
<li>Strong background in machine learning, computational modeling, and data-driven research methods.</li>
<li>Experience applying statistical methods, causal inference, and counterfactual explanation approaches.</li>
<li>Experience working with complex datasets relevant to human behavior, communities, or mobility patterns.</li>
<li>Demonstrated ability to conduct independent research and contribute to collaborative research projects.</li><br>
</ul><p></p><p><b>Bonus Qualifications</b>
</p><div><div><div><div><ul><li>Research experience related to AI models of communities, computational social science, or social behavior modeling.</li>
<li>Experience working with multimodal behavioral data or community mobility datasets.</li>
<li>Familiarity with research on the societal impacts of generative AI.</li>
<li>Experience contributing to manuscripts for publication.</li>
<li>Publication record in high-impact journals or conferences such as AAAI, IJCAI, AAMAS, KDD, or CHI.</li><br>
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</div></div><p></p><div><div><div><div><div><div><div><div><div><div><div><div><b>Years of Work Experience Required</b></div> <div>0</div>
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<div><div><b>Desired Start Date</b></div> <div>9/21/2026</div>
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<div><div><b>Internship Duration</b></div> <div>3 Months</div>
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<div><div><b>Position Keywords</b></div> <div>Computational Social Science, Human Behavior Modeling, Community Simulation, Generative AI, Causal Inference</div>
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Research Intern: Computational Social Science

<div><div><div>Job Number: P25INT-50
</div><div>Honda Research Institute USA (HRI-US) is seeking a research intern to work on an exciting machine learning challenge: understanding and modeling the objectives and dynamics that drive human team behavior. Current approaches focus on predicting team performance from observed interaction. This project aims to go a step further by modeling what teams are implicitly optimizing. The intern will develop computational models to draw on techniques such as inverse reinforcement learning, preference learning, or related ML methods, to uncover the latent objectives governing team dynamics such as information sharing, participation balance, and consensus formation. The project integrates ideas from multi-agent learning, collective intelligence, and behavioral psychology, and leverages real-world multimodal datasets of small group collaboration. The expected outcome is a research contribution suitable for submission to top-tier AI conferences (e.g., NeurIPS, ICML, ICLR), with potential impact on both human-AI interaction and multi-agent systems.
</div><div>San Jose, CA</div><br>
<p></p><div><div><div><div><div><p><b>Key Responsibilities</b></p><br>
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<p></p><div><div><ul><li>Develop computational models (inverse reinforcement learning (IRL) or preference learning models) to infer latent reward functions from team interaction data.</li>
<li>Design and implement theory-informed reward components inspired by collective intelligence and behavioral psychology (such as information sharing, participation balance, consensus dynamics).</li>
<li>Process and model multimodal interaction datasets, including conversational transcripts, temporal interaction signals, and computer vision features.</li>
<li>Generate and evaluate conterfactual or alternative interaction trajectories for IRL training.</li>
<li>Conduct experiments on team perfomance prediction, early outcome inference, and generalization across datasets.</li>
<li>Analyze learned reward strctures to identify distinct team strategies and behavioral patterns.</li><br>
</ul><p></p><p><b>Minimum Qualifications</b></p><br>
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<p></p><div><div><ul><li>Current PhD candidate in Computer Science, Electrical Engineering, Behavioral Psychology, or a related technical field.</li>
<li>Strong programming skills in Python and experiences with deep learning frameworks.</li>
<li>Solid understanding of machine learning fundamentals, including supervised learning and model evaluation.</li>
<li>Familiarity with sequence modeling (such as RNN, Transformers) or probabilistic modeling.</li><br>
</ul><p></p><p><b>Bonus Qualifications</b>
</p><div><div><div><div><ul><li>Proven publication record with reinforcement learning, inverse reinforcement learning, imitation learning, or preference learning.</li>
<li>Background in multi-agent systems, game theory, or sequential decision-making.</li>
<li>Experience with multimodal or time-series data.</li>
<li>Familiarity with natural language processing or conversational modeling.</li>
<li>Interest in human behavior, social interaction, or computational social science.</li>
<li>Prior research experience or publications in machine learning or related areas.</li><br>
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</div></div><p></p><div><div><div><div><div><div><div><div><div><div><div><div><b>Years of Work Experience Required</b></div> <div>0</div>
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<div><div><b>Desired Start Date</b></div> <div>9/8/2026</div>
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<div><div><b>Internship Duration</b></div> <div>3 Months</div>
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<div><div><b>Position Keywords</b></div> <div>Machine Learning, Affective Computing, Human State Sensing, Multimodal Data Analysis, Collective Intelligence</div>
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Research Intern: Affective Computing for Collective Intelligence