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Doctoral Associate Duke Department of Neurosurgery This Postdoctoral Associate position will play a critical role in making the use of new imaging tools more automatic and streamlined. Key responsibilities
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resonance energy transfer, siRNA mediated gene knock out, cellular imaging, qPCR, etc.) to elucidate unknown interactions between GPCRs and receptor tyrosine kinases at the cellular level. Analyses will focus
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an obtained doctoral degree. The successful candidate will work in a team dedicated to developing and using new approaches and methodologies for seismic imaging and Full Waveform Inversion using Generative AI
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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voiding behavior in mice and developed live imaging techniques to visualize sensory signaling in the bladder wall and dorsal root ganglion (DRG) neurons. In addition, we explore these pathways in human
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Assistant (Doctoral Candidate) with specialising in hyperspectral imaging (salary scale 13 TV-L, 75 %) The fixed-term position is for a duration of 36 month in accordance with the project duration. Your role
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was God who could grant a cure or prevent it. So how can we grasp healing in a world where there is neither a unified approach to it nor a coherent understanding? Despite the existence of a wide range
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adults, significantly impacting quality of life, and remain challenging to treat. Our research group has pioneered innovative methods to study voiding behavior in mice and developed live imaging techniques
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning