Sort by
Refine Your Search
-
Country
-
Employer
-
Field
-
and scholarship in language comprehension, multimodal communication, cognitive ageing, attention, prediction, speech perception, or related areas. Experience and achievement reflected in a growing
-
. 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
-
, multimodal sensing, LiDAR processing, or infrastructure monitoring is required. Familiarity with machine learning, large language models, agentic AI, AI-based perception, and data integration is important
-
mechanistic insights linking textural dynamics to sensory perception. Integrate multi-modal datasets from different mastication simulators into a 3D Principal Component Analysis (PCA) space, enabling
-
Overall Role Lead the end-to-end research and development of next-generation pipeline inspection and repair robotic systems, covering mechanical design innovation, perception and navigation
-
toddlers at risk for autism, recruiting and testing participants, as well as data processing and analysis. The main goals of SHAPE are to map out the relationship between the visual perception of shape and
-
Norway’s research group for User Perception and Engagement in XR Experiences. ND conducts research in networks and distributed systems of all scales, multimedia and AR/VR/XR systems, robotics and machine
-
characteristics, brittleness vs. plasticity, and frictional/lubrication properties. Generate mechanistic insights linking textural dynamics to sensory perception. Integrate multi-modal datasets from different
-
and Distributed Systems Research Group (ND) with co-supervision from IFI’s Machine Learning section and the University of Inland Norway’s research group for User Perception and Engagement in XR
-
/Research Fellow(SRF/RF) to carry out research in robotics and machine learning by exploring cutting-edge approaches such as learning-based robot perception, adaptive control with reinforcement learning