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. The project focuses on developing novel representation learning and generative modeling methods to construct a unified cellular morphology state space across heterogeneous datasets. By leveraging shared
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lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based
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to the development of advanced AI-based techniques for radiofrequency spectral monitoring across a wide range of highly relevant applications. These applications include, but are not limited to, anomaly detection
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candidate will lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based
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acquisition and clinical interpretation. The aim of this postdoctoral project is to develop and evaluate AI‑based methods that reduce operator dependence in ultrasound imaging, with a focus on clinically
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dynamic and supportive research environment with access to world-class infrastructure, strong clinical links, and excellent opportunities for scientific development Duties The successful candidate will lead
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higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. The position requires undertaking
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as climate change, Earth system science, air pollution, natural hazards, and monitoring of the Earth's surface. We have extensive experience in developing and applying remote sensing across
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Description This postdoctoral position is part of the research portfolio within Mechanical Engineering and Product Development, where multiple externally funded research projects are conducted in parallel in
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embedded into a cutting-edge research project dedicated to the development of advanced AI-based techniques for radiofrequency spectral monitoring across a wide range of highly relevant applications