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to have a strong interest in data analysis, and medical research, along with relevant academic background and skills within medical image analysis and machine learning that will enable them to contribute
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embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy) to evaluate performance
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and indexing) carrying out data collection, data management and analysis (under supervision) performing research tasks within the area of classical archaeology, e.g. literature searches and image
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of the project is to bring about a paradigm shift in the design of switchable materials, by delving into the uncharted territory of f -element valence change (VC). The VC materials will be constructed using rigid
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on both public transport, walking and bicycle trips focusing on the full door-to-door travel. The data sources include image data in terms of photos and videos of the urban landscape as well as in and
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in situ cells, which can be used for operando diffraction and imaging experiments on metal-air batteries during charge/discharge. Perform detailed materials characterization of the components
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can be used for operando diffraction and imaging experiments on metal-air batteries during charge/discharge. Perform detailed materials characterization of the components of the battery using e.g. in
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that paradigm and explores negative ion mode LC-MS/MS to reveal new molecular insights and enable broader biological discovery This PhD position is part of a vibrant research environment involving other
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pre-training earlier), as well as in counterbalancing bias & overfitting. In addition to classical XAI models, e.g. decision trees, there is the paradigm of commonsense knowledge (CSK), i.e. everyday
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will include versatile, fast-moving tool-carrier robots, drones for aerial view and selected aerial manipulation, and specialized, slow-moving prototype robots. (PhD#2) Robotic Implements. Study and