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-inspired computing paradigm with the potential to drastically reduce energy consumption while enabling faster inference than conventional digital architectures. A major challenge, however, is the development
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lipid nanoparticles designed to modulate local immune responses. To address these questions, you will use a broad range of state-of-the-art techniques, including flow cytometry, advanced imaging
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PhD candidate, you are expected to bring your own creativity to the challenge: How can image-based AI analysis reveal drug effects? How can mechanistic computational models develo on spheroid morphology
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machine learning and physics to recover nanoscale information from imperfect images? Modern computer chips are built with features only a few nanometers across, yet manufacturers need to measure these
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Our team We develop next generation optical imaging and sensing platforms in the context of future biomedical, pharmaceutical and clinical applications. We are particularly interested in label-free
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focused on the development of next-generation compact hyperspectral spectropolarimetric cameras. Hyperspectral imaging enables simultaneous acquisition of spatial and spectral information, supporting
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households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data
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fundamental learning procedures to tackle distressing images related to aversive memories. The aim is to generate insights with direct impact on clinical practice and patient wellbeing. PhD Candidate Reducing
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mitotic errors in human IVF embryos from live cell imaging data. In addition, you will contribute to teaching activities (maximum 0.1 FTE) and supervise Bachelor and Master students. This project is
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prediction of DNA replication defects and mitotic errors in human IVF embryos from live cell imaging data. In addition, you will contribute to teaching activities (maximum 0.1 FTE) and supervise Bachelor and