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collected in Denmark. The project is led by Associate Professor Kim Andersen and includes Associate Professor Lene Heiselberg, Professor David Nicolas Hopmann, a postdoc, and the PhD student. The PhD student
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the lifespan. About the project: Using Magnetic resonance imaging (MRI) we are able to assess how brain regions are connected (structural connectivity) and communicate (functional connectivity). In this project
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Postdoc Positions Application Deadline 27 Apr 2026 - 00:00 (Europe/Warsaw) Country Poland Type of Contract Permanent Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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-mode trap instruments supporting both collisional- and electron-based dissociation Contribute to spectral annotation workflows in collaboration with a computational postdoc Disseminate findings through
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developing prototype drug candidates. Project outcomes will be protected as intellectual property and will provide a foundation for translation through clinical studies and appropriate commercialisation
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, heritage science, or a closely related scientific discipline. • Good understanding of relevant analytical methods and instrumentation (e.g., spectroscopy, microscopy, and/or imaging techniques
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background in neurophysiology / imaging and animal behavior to join the Whitlock group. The position is financed internally by NTNU and is part of a new line of work investigating the neural population coding
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of biologically and disease-relevant questions. We apply these advanced imaging technologies to clinically meaningful model systems in order to evaluate their robustness, scalability, and added value
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 months ago
Traitement du Signal et des Images, 2023. Douté, S., Forbes, F., Borkowski, S., Meyer, L., Heidmann, S., 2024. Massive analysis of multi-angular images by inverse regression of reflectance models