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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning
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and Space Weather. The successful candidate will contribute to the development, testing, and operation of solar monitoring stations, real-time data pipelines, and AI-based analysis tools. The position
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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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» Electrical engineering Engineering » Other Researcher Profile First Stage Researcher (R1) Country Estonia Application Deadline 26 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Full-time Hours
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Portugal Application Deadline 6 Nov 2025 - 00:00 (Europe/Lisbon) Type of Contract Permanent Job Status Full-time Hours Per Week 35 Offer Starting Date 24 Oct 2025 Is the job funded through the EU Research
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of this annex, as well as to: Programming in Python and R. Statistical classification and machine learning methods: SVM, neural networks and logistic regression. 3.2. Qualification: Official Master’s degree in
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, the selected researchers will deal with: Research & Development: Designing, developing, and implementing state-of-the-art machine vision and deep learning algorithms to analyze complex image and sensor data
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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driving, in-car monitoring, industrial automation, and security surveillance. The research, called "R4DAR," aims to leverage emerging 4D imaging technology with Massive MIMO to create image-like radar