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to develop a conceptually new type of image sensor based on perovskite materials. Related publications: 1. Tsarev, S., et al., Vertically stacked monolithic perovskite colour photodetectors. Nature, 2025. 642
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methods for image classification including machine learning and deep learning. You will develop clear workflows that allow for regular update of the derived models and maps. Furthermore, you will work
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-PDF), data analysis and modelling for partially crystalline material systems Developing lab-based X-ray multimodalities: X-ray diffraction & imaging presentation of scientific results through
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-cultural. EPFL covers a wide spectrum of science and engineering and offers a fertile sSchool of Basic Sciences include theoretical and experimental biophysics, biological imaging and physics of machine
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conceptually new type of image sensor based on perovskite materials. Related publications: 1. Tsarev, S., et al., Vertically stacked monolithic perovskite colour photodetectors. Nature, 2025. 642(8068): p. 592
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Universität Zürich About this site Contact Data Protection Statement Close Image Overlay Close Video Overlay [%=content%] [%=content%] [%=content%] back Overview Page[%=text%] [%=content%] Close Menu [%=text
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This project is a collaboration between the Multiphase Fluid Dynamics group (Prof. Supponen) at the Institute of Fluid Dynamics, and the Functional and Molecular Imaging group (Prof. Razansky
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100%, Zurich, fixed-term The enhancement of endoscopic videos is a critical yet complex task. These videos are integral to various clinical interventions, where the clarity of imaging data is
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a focus on how cell elimination underlies the emergence of functional tissues. The team employs a wide range of cutting-edge methods, including single-cell transcriptomics, imaging-based techniques
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collaboration to combine their expertise. By integrating these complementary approaches, we aim to provide the first comprehensive picture of Hsp90's structure–dynamics–function relationship, with broad