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on the development, optimization, and clinical evaluation of new x-ray-based imaging methods. The lab focuses on the use of medical physics approaches to improve image acquisition methods and processing algorithms
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is to investigate localized flow and crystallization processes of PCMs in devices under different conditions with advanced 3D imaging tools like CT and NMR imaging, and to couple this with performance
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characterizing defects such as dislocations Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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adipose tissue. In particular, we will study the role of different membrane receptors and their signaling pathways in the browning process. The various techniques used will include cell biology and genetic
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, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities Collaborating closely with experimental partners to validate methods and
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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candidate with experience in cell culture, stem cells, molecular biology, immunohistochemistry, biochemistry, protein purification and/or confocal imaging. Experience in using human tissue is advantageous. A
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The Department of Chemistry invites applicants for a PhD fellowship in nanochemistry and ultrafast spectroscopy. The project is part of the research project “Subcellular multiplex imaging with
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning