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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated
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, or a related discipline. Hands-on research experience in one or more of the following areas will be considered an advantage: Confocal microscopy and Image processing Optical bench instrumentation
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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important question to solve, as DNA damage-stalled RNA polymerase causes bigger problems for the cells than the actual DNA damage itself. In this project we will use innovative single molecule imaging
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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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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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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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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focus on understanding how axons maintain their structure and function, and how these processes break down in disease. You will have the opportunity to contribute to one of our ongoing projects addressing