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capable of understanding, learning, and acting in complex, dynamic settings. The lab’s work lies at the intersection of computer vision, multimodal learning, and robotics, advancing next-generation embodied
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teammates to develop experimental methods, and helping develop novel hypotheses about the visual perception of shapes. The data collection work involves posting timeslots on SONA and running in-person
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systems capable of understanding, learning, and acting in complex, dynamic settings. The team works at the intersection of computer vision, multimodal learning, and robotics to create next-generation
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical
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sciences » Philology Chemistry » Physical chemistry Environmental science » Earth science Computer science » Computer systems Computer science » Computer systems Computer science » Computer systems Computer
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environments This PhD project investigates the use of digital technologies (environmental sensing, user feedback loops, computer vision, machine learning) and theories of human perception and behavioral nudging
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researchers from IDLab-AIRO (robotic experts) and imec. Your main tasks include: Reviewing literature on decentralized control frameworks in the domain and machine learning algorithms compatible with
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Science. Commitment to undergraduate and graduate education. Demonstrated expertise in machine learning/deep learning and software development (Python; PyTorch/TensorFlow). Peer-reviewed publications and strong
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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for drone swarms. The role will focus on multi-agent visual perception techniques. Group website: https://personal.ntu.edu.sg/wptay/ Key Responsibilities: Develop signal processing and machine learning