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environment. The student will investigate the properties of fluorescent probes and how we can use them to develop advanced imaging strategies for the study of cellular processes. The project will focus
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, multiplexed imaging tools to develop spatiotemporal protein-maps of human cells and tissues, under the umbrella of The Human Protein Atlas (HPA). The project focuses specifically on understanding dynamic
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(MRE) for the brain is a technique of magnetic resonance imaging (MRI) in which mechanical properties of the brain tissue are estimated non-invasively. Our group is creating next-generation MRE tools
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this process is a 3D reconstruction of the given molecule at high enough resolution to resolve individual atoms. However, the images obtained from the electron microscope are often plagued by high levels
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the masses. Meanwhile, developing software for an AI-powered automated imaging farm, which involves robotic and imaging hardware control, experiment planning, and real-time AI-powered data analysis. You will
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Making Under Deep Uncertainties (DMDU) project aims to shift the paradigm from a traditional 'predict-then-act' model, which optimizes based on a single 'best-guess' future (forecast), to a model that
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in collaborations with clinicians. You will drive projects involving wetlab, microscopy and image analysis of clinical tissue samples. The position is mainly funded by an EU project, DISCERN, aiming
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Fredrik Kahl (computer vision and image analysis at Chalmers), Kathlén Kohn (algebraic geometry at KTH), and Mårten Björkman (robotics, perception and learning at KTH). The Department of Mathematics at KTH