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ability to express yourself both orally and in writing Computer literacy (MS-Office; Imaging Software) Basic experience in academic writing Didactic competences / experience with e-learning Excellent
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• Computer literacy (MS-Office; Imaging Software) • Basic experience in academic writing • Didactic competences / experience with e-learning • Excellent command of written and spoken English (C1 Level
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activities in the field of cardiovascular, exercise and/or environmental and physiology; the applicant should have experience and interest in research using magnetic resonance imaging methods; participation in
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, the encoded motion data is built using dynamic motion primitives. A Machine-Learning approach is trained that takes into account both the encoding motion data and the image data to adapt learned skills
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are learned using a fixed procedure, and the latent variable has high dimensionality. Recently, diffusion-based generative models have proven successful in image processing, in reinforcement learning and
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movement development. We collect and analyze three-dimensional motion capture data and medical images from various groups, including clinical populations, healthy people and elite athletes. Our goal is to
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shape higher-order computations in the brain. To address these questions, we use multiple-cell recording, subcellular patch-clamp techniques, Ca2+ imaging, cutting-edge light microscopy (confocal, two
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multidimensional flow cytometry or in confocal imaging. What we offer: Work-life balance: Our employees enjoy flexible working hours and can partially work remotely. Inspiring working atmosphere: You are a part of
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experts at the Computational Imaging Research Lab (CIR ) and the Department of Cardiology at the Medical University of Vienna and Visualization and Data Analysis Research Group (VDA ) at University
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Dark Matter discovery experiments. A research group works on R&D of particle detectors for those experiments, as well as for its applications in medical imaging. For that it collaborates with MedAustron