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Field
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Associate to undertake fundamental and applied research into novel deep learning approaches to analysing LiDAR and vision data in forest imaging and remote sensing. The role is part of a three-year project
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– Gulbenkian Institute for Molecular Medicine is a pioneering Portuguese private research foundation with an ambitious vision for fundamental scientific exploration and its profound impact on global health and
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of Engineering at the University of Texas at Arlington invites applications for a rank Postdoctoral status. This position will focus on analyzing imaging data, developing novel methods for analysis and integration
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype
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Position: Postdoctoral Researcher in Vision-Language-Action Models and Physically Informed Neural Networks for Surgical Robotics Publication date: 30.09.2025 Closing date: 29.10.2025 Level of education: PhD
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on safety, cooperation, and efficiency in human-robot teams. Multi-Modal ML: Expertise in working with diverse data types, such as vision, speech, images, and physiological signals. Experience integrating
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recording, motion capture, eye tracking, physiological measurements, various types of brain imaging (EEG, fMRI), and rapid prototyping and robotics laboratories. Applicants are required to upload a research
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significant mathematical and computational background to learn and develop new AI methods. While demonstrated experience in computer vision and deep learning for pathology or other biomedical image analysis is
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unique device structures, apply thermal imaging techniques for nm-scale devices and access the extensive device testing suite available in Bristol, from advanced electrical, thermal to electric field
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of 360-degree vision and robotics (collaborative robot arm, humanoid robot) to enable the dynamic handover of an object from a person to a robot and carry it together to move it. - Activity 1: Substantive