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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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? No Offer Description Help develop explainable AI to support human-AI interactions. As a Postdoctoral Researcher at TU Delft, you will investigate how multimodal AI can understand, explain and
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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
this context, the offer is about continual audiovisual robot perception, and aims to develop methods and algorithms to continuously extract cues about human behaviour from audio and visual data in real-world
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applied AI research, contributing to one or more of the three thematic working groups: 1) Integration and extraction of actionable information from multimodal spatiotemporal data, including physiological
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analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Proven experience with reinforcement learning algorithms and implementations. Experience in
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perception, dynamics and control, and human factors. Website: intelligent-vehicles.org , YouTube: youtube.com/c/IntelligentVehiclesatTUDelft We are seeking a highly motivated researcher to contribute
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-driven research within the interdisciplinary spectrum of machine perception, dynamics and control, and human factors. We are seeking a highly motivated researcher to contribute to the development of next
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multimodal interaction. Our research aims to bridge the gap between visual perception and actionable assistance, with applications including: · Video-based skill coaching and instruction
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Job Purpose To make a contribution to an ERC-funded project Dynamic network reconstruction of human perceptual and reward learning via multimodal data fusion, working with Prof. Marios Philiastides