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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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results. You will join the Section for renewable energy and forest sciences and conduct cutting‑edge research using remotely sensed point‑cloud and image data to map forests. The position is part of two
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and Transmission Electron Microscopy studies at the Department of Physics. This PhD position is within the field of experimental materials physics and nanotechnology, in the project «Nanoscale imaging
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position is within the field of experimental materials physics and nanotechnology, in the project «Nanoscale imaging of magnetic skyrmion dynamics in thin film devices» (NIMSKY). Magnetic skyrmions
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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, integrating with Galaxy as well as solutions for secure and trusted exchange of encryption keys. This development is part of a collaboration in the ELIXIR context, which also in-cludes core European data
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characterize forest structure. Remote sensing data, such as images, lidar, and photogrammetric point clouds acquired from drones, aircraft, and satellites, will play a central role in the development
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(imaging, 3D point clouds, or multispectral data). Good presentation skills, written and oral in English Qualifications considered an advantage Background in plant genetics/genomics/phenomics (SNP analysis
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will focus on integration of the extensive amount of data collected by forest machines with remote sensing data to further characterize forest structure. Remote sensing data, such as images, lidar, and
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“Female Heart”. The researcher position has a temporary financing for two (2) years from 01.11.25. About the project/work tasks: Advanced image analysis of ultrasound images Statistical analysis