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in research related to NIH and industry funded projects in nuclear medicine imaging and dosimetry. This includes clinical trials in radiopharmaceutical therapy for prostate cancer and neuroendocrine
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applicant will have a PhD in vision science, computer science, or a related field. Experience in cloud-based and mobile image processing for rapid object and face recognition and in use of head-mounted eye
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programme of work Carrying out a range of experimental protocols, including tissue culture of human pluripotent stem cells, embryo model generation, imaging, molecular characterisation etc. Fully analysing
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motivated Post-doctoral Fellow with a background in neurophysiology and animal behavior (including two-photon imaging in behaving animals) to join the Zong group at the Kavli Institute for Systems
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generated large-scale, multimodal datasets spanning neural imaging, electrophysiology, behavior, and molecular profiling. While these data offer unprecedented opportunities to understand brain function
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Center for Devices and Radiological Health (CDRH) | Silver Spring, Maryland | United States | about 1 hour ago
the assessment of artificial intelligence models applied to whole slide images, with a focus on CPATH imaging AI pipeline and cutting-edge technologies such as vision and language foundation models in
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imaging; and preparing research reports, abstracts, and figures for presentations and publications. This role offers the opportunity to develop essential research and data management skills using real-world
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to both contribute to new advances in a field with real world applications. Your research focus will be on challenges related to computer vision for geophysical image data. You will investigate and develop
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NeuroVision – Neuromorphic Vision Sensor Data Coding. The project focuses on the development of efficient, low-latency, and scalable coding solutions for event-based (neuromorphic) vision sensors, targeting
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are also expected to drive a paradigm shift in medical device security regulation, moving evaluation away from individual components and toward system-level thinking across vast networks of interacting