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detailed data about forest ecosystems. To convert the captured data into meaningful information about the forest environment we seek a PhD candidate who wishes to advance state-of-the-art computer vision
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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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Nordisk Foundation (NNF) New Exploratory Research and Discovery grant entitled: Information Theoretic Disentanglement of the Exceptional Biological Learning Machine, which is headed by Professor Jan
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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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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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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 Science or equivalent (with a focus on Machine learning, Artificial Intelligence, Learning Technology or alike). Your course of study must correspond to a five-year Norwegian course, where 120
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accurate information. Machine learning allows for the systematization and processing of this data into new forms of information to support the management of forest resources and ecosystems. The PhD candidate
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a background in deep learning and computer vision? Are you
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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