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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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the genetic factors influencing changes in brain structures, using brain imaging, computational and statistical methods of network science. Project Aim: The aim of the project is to uncover the complex
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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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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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) 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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awareness capabilities. The PhD will involve both simulation and experimental work. This includes designing and testing optical instrumentation and conducting observation campaigns to image and track
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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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strong background in biological sciences who are enthusiastic about contributing to cutting-edge research. The Cao Laboratory utilizes and develops cutting-edge single cell omics, genetic, imaging and
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Ultrafast lasers drive innovations from quantum technology to medical imaging, yet controlling femtosecond pulses remains a major challenge. Metamaterials are artificial structures with