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configurations and illumination conditions. Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize
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quantitative modeling; Strong expertise in programming, including proficiency in languages commonly used in data analysis and machine learning, such as Python; Excellent verbal and written communication skills
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information For further information, please contact Prof. Ants Kallaste ants.kallaste@taltech.ee and Prof. Anton Rassõlkin an- ton.rassolkin@taltech.ee or visit https://taltech.ee/en/electrical-machine-group
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» Other Researcher Profile First Stage Researcher (R1) Country Switzerland Application Deadline 30 Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 41 Is the job funded
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. The candidate will also collaborate with the Department of Computer Science at Kiel University and the remote sensing company EOMAP GmbH, employing state-of-the-art machine learning techniques to improve
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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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data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research findings
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18 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Physics
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smart monitoring methods can be used to investigate the ecological status of smaller -often unmonitored- water bodies. These water bodies make up one-third of the total number of water bodies in
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one month and 30% within one year after hip fracture surgery. It is therefore crucial to monitor patients’ health condition continuously and accurately after surgery to measure and evaluate patients