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Field
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, vegetation occlusion, and low illumination conditions; - Contribute to the definition of resilient perception architectures for outdoor robotic operation. 2. Machine learning-based resilient perception
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developing learning-based perceptive approaches. You will develop learning and/or optimization based approaches to multi-robot path and motion planning and coordination, integrating multi-modal perception and
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advanced machine learning models, including multimodal perception and foundation model based systems, within distributed data, training, and inference pipelines. Perform hands-on research engineering at
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science; Biotechnology Project funding Project based
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-learning models on resource-constrained hardware Candidates should also have experience or strong interest in Edge AI, TinyML, embedded machine learning, neuromorphic computing, or signal processing. We
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their 4th or 5th year of studies (M1, M2 or gap year) - Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework
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the consequences of actions, and adapt reliably when the physical world changes? The project connects multimodal perception, reasoning and action with predictive learning and edge intelligence. Research directions
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experimentation, machine learning, and laboratory automation. Other tasks as assigned. Basic Qualifications Master's degree or higher in Robotics, Mechanical Engineering, Electrical Engineering, Computer
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, ECG, GSR and other biosignals. Multimodal data analysis Machine learning and advanced statistical modeling Research experience in perception, consciousness, cognition, or emotions will also be valued
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-UAS technologies o Multi-agent systems and swarm robotics o Autonomous navigation and guidance o Computer vision and perception o Machine learning