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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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strategies. The research group focuses on exploration of tumor immune microenvironments through spatial omics and imaging, development of computational models for prediction of molecular and clinical features
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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Description of workplace Diangostic Radiology, ITM, Lund University conducts research related to medical imaging diagnostics. LUCI (Lund University breast Cancer Imaging) is a cross-disciplinary
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The Department of Diagnostics and Intervention is looking for up to two postdoctoral research fellows for projects focusing on imaging of brain plasticity, with applications towards aging and
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and molecular genetics as well as hands-on experience with cloning, live-cell fluorescence microscopy, image analysis, and sample preparation for sequencing and multi-omics analyses. The main model
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systems for diagnostics and treatment. Core activities include signal processing, antenna design, and measurement hardware development. Building complete prototype systems for clinical testing is a central
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for functional reactor or fluidic systems. Experience with high-speed imaging, pressure measurements, or cavitation evaluation. Demonstrated ability to work independently while maintaining strong collaborative
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immune cells. Several of the imaging setups are partly developed in-house and used in conjunction with established expertise in physiology to enable longitudinal in vivo studies. In addition to the high
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and material inventories. Automate: Develop methods to automate the collection and harmonisation of data (type, age, condition) using images, building plans, and synthetic data. Validate: Conduct case