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at the intersection of biology, and medicine? We’re seeking a talented Scientist to join our dynamic team! Our mission is to develop novel PET imaging agents for widely metastatic cancers and to develop novel
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for automatic segmentation and morphometry of histological images; - Compare the predictive value of AI-driven image analysis with clinical and biomarker data; - Collaborate with international experts in medical
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Pro-Vélo program, which promotes sustainable commuting. This PhD project aims to understand how the protein KIF2C, a microtubule depolymerase, regulates the fidelity of cell division by forming
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using deep learning and AI-driven image analysis. You will: - Analyse pre-implantation kidney biopsies according to the Banff criteria; - Apply AI methods for automatic segmentation and morphometry
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at the Division of Cell Biology, Neurobiology and Biophysics external link within the Department of Biology external link . Our division hosts the state-of-the-art Biology Imaging Center external link , which
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focus on image processing and restoration, to develop novel AI-based approaches to restore and denoise Transmission Electron Microscopy (TEM) images. This position is part of a cross-disciplinary research
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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THR demand in younger patients expected to increase fivefold by 2030, revision surgeries will also rise. To improve implant positioning, image-guided navigation is increasingly used in complex THR
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. - Have skills in image analysis (segmentation, cell tracking, spatio-temporal quantification) and quantitative analysis of biological data. - Familiarity with classical approaches in protein biochemistry