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Field
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. About the project CLIPEUS aims at creating a communications network among man-made implantable devices and the natural cells inside the body under the novel communication paradigm called Internet of Bio
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disease. Research methods include behavioral, functional, and structural magnetic resonance imaging (MRI) measures. Job description: The successful candidate will receive interdisciplinary training in
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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approaches, we aim to provide the first comprehensive picture of Hsp90's structure–dynamics–function relationship, with broad implications for understanding cellular health and disease. As a PhD student on
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like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy
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synthesizing detectable targeting ligands for engineered immune cells and image and quantify the target cell engagement and efficiency. The project is highly multidisciplinary and involves aspects of synthetic
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performance; Perform and Analyse Particle Image Velocimetry data in shear controlled continuous experiments; Developing guidelines for controlling negative effects of SACs in anaerobic wastewater treatment
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. Biomolecular condensates have emerged as a new paradigm to understand biological functions in living cells. Dresden has pioneered research in the field of biomolecular condensates and developed into its vibrant
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student at the University of Paris-Saclay attached to the doctoral school of Particles, Hadrons, Energy and Nucleus: Instrumentation, Image, Cosmos and Simulation (PHENIICS). This work will be carried out
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experiments, as well as for its applications in medical imaging. For that, it collaborates with MedAustron, an ion cancer therapy and research center. Within this collaboration, we design and develop ultra-fast