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Field
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planning and learning from interaction. Possible directions include learning from video, demonstrations and simulation, and transferring knowledge across robot configurations. Adaptation and edge
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multidisciplinary environments Curiosity-driven and self-motivated working attitude Knowledge of biomechanical modeling, anatomy, vision-based motion capture, machine learning, control systems Keep in mind
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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conferences and contributing to collaboration between NIBIO, NMBU, and national and international research partners. Professional qualifications (required) A Master’s degree in machine learning, artificial
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, longitudinal modelling, machine learning and multivariate approaches. Proficiency in programming (e.g., MATLAB, Python, or R), handling large datasets, and working with complex analysis 2 pipelines is an
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, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. About the project The role of the PhD candidate will
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of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access to research vehicles and advanced radar prototypes
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
conditions, using techniques from computer vision, natural language processing, and representation learning [4,5]. A third objective will be to study how sign-language videos can be represented in a way that
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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. As a PhD researcher, you will unravel the atomic-scale mechanisms of hydrogen embrittlement in compositionally complex recycled steels, using density functional theory and machine-learned interatomic