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Field
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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to train tomorrows leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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for communication and computing in highly dense cellular networks and for infrastructure-less wireless services. Successful applicants will work on algorithms and techniques to make Safe Machine Learning available
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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implementing AI algorithms to deliver safer and more efficient care. The student will have access to a unique training programme in AI in healthcare and health data science as well as a wide range opportunities
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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Programme? Horizon Europe - MSCA Reference Number BAP-2025-685 Marie Curie Grant Agreement Number 101226491 Is the Job related to staff position within a Research Infrastructure? No Offer Description
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8 Nov 2025 Job Information Organisation/Company ETH Zürich Research Field Computer science » Other Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile First