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the synchrotron-based imaging technique Dark-Field X-ray Microscopy and together we utilize it to visualize the evolution of internal structures in metals during plastic deformation, i.e. changes in shape due
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equivalent) in Allergology, Immunology, Bioinformatics, Biochemistry, Biomedical, Protein Chemistry, Molecular Biology or a related field as well as: Solid understanding of immunology and/or food allergy, and
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complete picture of fish habitat use and connectivity. The PhD is part of the section for Ecosystem based Marine Management and the Marine Habitats research group, as well as several synergistic initiatives
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group is a highly ambitious group, pursuing research on microfabrication of 3D polymer and carbon structures and devices and their application in biomedical applications, bioelectrochemistry and
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described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural Technology. Our technologies and solutions are developed
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of electron microscopy imaging and spectroscopy to reveal the structure–property relationships that govern molecular adsorption mechanisms. This interdisciplinary project is fully funded by DTU’s PhD grant
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: Specialization in machine learning and computer vision. Strong skills in developing computer vision and AI models. Experience with thermography or thermal imaging. Experience in data fusion and multimodal learning
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thermochemical TES. Your main supervisor will be Prof Adriano Sciacovelli and you will join the Thermal Energy Section at DTU Construct. Your work will contribute to a paradigm shift in how complex TES systems
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described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural Technology. Our technologies and solutions are developed
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innovative services and products which benefit people and create value for society. DTU Health Techs expertise spans from imaging and biosensor techniques, across digital health and biological modelling