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Field
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning methods with the support of
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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector
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principles, lab measurement, computer vision and ArcGIS, potential fieldwork and UAV flying training. Person specification Experience and/or enthusiastic interest in one or more of the following areas
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electromagnetic device performance analysis>>>Embedded SystemsResearch focuses on:•Robotics, computer vision, and machine learning/deep learning•Wearable and implantable sensors, biosensors, and tele
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AI, human-AI collaboration systems, computer vision (CV) models, and natural language processing (NLP) models. Candidates with expertise in any of these areas are encouraged to apply, as the position
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in a field related to one of the three research areas of MCML: Foundations of Machine Learning; Perception, Vision, and NLP; and Domain-Specific Machine Learning. The Munich Center for Machine
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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of insect pollinators from large-scale photographic and video datasets. The research will integrate ecological fieldwork, computer vision and stakeholder engagement to: 1.Develop and optimise deep learning