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diagnostics (theranostics), focused ultrasound (FUS), and molecular imaging (PET/SPECT). The PhD candidate will be responsible for conducting preclinical studies using animal models of glioblastoma, including
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utilizing human cell cultures (2D and organoids), advanced fluorescent imaging, live imaging, FACS, RNAseq + bioinformatic analysis, Click-IT technology, RT-qPCR, Western Blot, and possibly animal experiments
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questions related to the molecular regulation of autophagosome formation, using cell biological, genetic, and imaging-based approaches. The candidate will explore the function and regulation of proteins
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for clinical AI based on patient data from heterogeneous sources notably language/speech-based sources. The activity will focus on the development of a prototype implementation of early warning- and other AI
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interdisciplinary team will develop a machine learning based monitoring system that leverages spoken language processing (SLP) and natural language processing (NLP) of speech recorded at home to calculate relapse
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of three topics: 1. Combining synthetic aperture radar (SAR) images with probabilistic weather prediction models to view and predict dynamic sea ice properties. 2. Using multi-frequency SAR, coupled with in