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within the IROKO/INRIA team in Montpellier. The doctoral student's primary mission will be to **develop, adapt, and evaluate methods for estimating sample influence** within the specific context of Pl
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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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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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institutes as well as a secondment in a private-sector partner of the network. The research topics focus on the development of new physics inspired AI methods for inverse problems, uncertainty estimation and
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to learn laboratory methods for analysis of relevant BGC parameters. Training: You will be based in the Polar Oceans Team at British Antarctic Survey, a highly active research team focused on both
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» Science communication Computer science » Other Engineering » Design engineering Technology » Computer technology Technology » Future technology Arts » Visual arts Computer science » Informatics Researcher
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the PhD program in Computer Science and Computer Engineering with specialisation in Information Systems (IS). In the context of Prof. Fridgen's PayPal-FNR PEARL Research Grant and in strong synergy and
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group of scientists and medical professionals, mainly focused on innovative clinical imaging methods in breast cancer diagnostics, with a particular focus on cancer screening and artificial intelligence
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Primary Supervisor -Prof Michal Mackiewicz Scientific background Marine litter is a key threat to the oceans health and the livelihoods. Hence, new scalable automated methods to collect and analyse
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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