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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
develop our state-of-the-art imaging systems by optimizing the hardware and integrating real-time data processing and analysis through machine learning techniques to achieve precise characterization
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techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical
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for personally experienced past events, or episodic memory (EM), using a combination of behavioral, neuroimaging (fMRI) neurophysiology (EEG), and neuromodulation (TMS) methods. Traditional imaging studies focused
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laboratory is seeking a motivated and highly skilled post-doctoral researcher to join our dynamic laboratory dedicated to studying HIV-1, with a particular focus on development of novel imaging approaches
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of expertise, ISSS aims to offer the full portfolio of expertise in the fields of signal and image processing, novel manufacturing technologies, microsystems, microwave engineering, mobile communications systems
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or orthotopic tumour models Supporting preclinical treatment studies involving standard-of-care or experimental agents Applying in vivo imaging techniques (e.g., bioluminescence imaging) to monitor tumour
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in sustainability, photonic and quantum technologies, new nanostructured materials, sensing, imaging and clean energy. The group adopts an interdisciplinary approach to provide leading-edge research in
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and assessment of ocular inflammation models, such as intravitreal injections, ocular imaging techniques and flow cytometry. Moreover, applicants will perform a variety of additional procedures such as
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Microanalytical and Imaging Consortium (SENCR-MIC) and Interdisciplinary Materials Research and Education Laboratory (IMREL). Primary Purpose of the Position: The position’s primary purpose is to conduct research
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence