​We are seeking a research intern with a focus on representation learning and semantic reasoning for multi-agent robotic systems. The ideal candidate needs to have a strong background in machine learning/deep learning with focus on Representation Learning, Semantic Representation, Semantic Reasoning, and Multi-Agent Reinforcement Learning (MARL).

During the time of the internship, you are expected to:

• Explore and develop novel techniques in representation learning and semantic reasoning with the focus on the multi-agent robotics domain.
• Publish your research findings in top-tier conferences and journals.

• M.S./Ph.D. candidate in computer science, electrical engineering, mechanical engineering or similar fields.
• Previous experience in machine learning and deep learning research, particularly in the field of representation learning and semantic reasoning.
• Excellent programming skills in Python, C++.
• Experience with Machine learning libraries such as TensorFlow, PyTorch.

• Familiarity with semantic reasoning frameworks, knowledge graphs, semantic datasets, etc.
• Familiarity with Multi-Agent frameworks like Multi-Agent RL, POMDP, etc.
• Experience working with large datasets and implementing efficient data preprocessing and augmentation techniques.
• Familiarity with state-of-the-art techniques in representation learning, such as autoencoders, variational autoencoders, generative adversarial networks (GANs), or self-supervised learning methods.

​Representation Learning, Semantic Reasoning, LLM-Embodied Agents, POMDP, Multi-agent Reinforcement Learning with Communication (Comm-MARL)