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? Join our team to develop next-generation multi-aperture ultrasound technology combining distributed sensing, adaptive acquisition, image reconstruction, and AI-driven imaging for groundbreaking clinical
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months agoPostdoctoral Position: Computational Genomics · AI-Driven Precision Oncology · Translational Cancer Biology and Immunobiology Wang Laboratory, UPMC Hillman Cancer Center Department of Pathology and
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available at the Department of Informatics (IFI), UiO. Do you have a background in energy informatics, computer science, or energy systems, and are you are interested in edge intelligence, multi-agent AI, and
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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complex multi-omics, imaging, and clinical datasets, contributing to research in cardiovascular systems biology and precision medicine. The Data and AI Researcher II will benefit from the highly
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to: · Characterize the genomic, epigenomic, and transcriptomic landscapes of synovial sarcoma using single-cell multi-omics approaches. · Investigate the epigenetic evolution of tumors and mechanisms underlying
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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datasets with multi-omics profiles of tumors from previous studies Developing new machine learning models through collaboration with the Swiss Data Science Centre Establishing data analysis pipelines
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in edge intelligence, multi-agent AI, and interdisciplinary collaborative research, and looking for a PhD position, this opportunity can be for you. The Energy Informatics EI@ND Networks and
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of computing and healthcare. Methodologies of interest include: multi-modal learning, foundation models, including large language models, agentic AI, multi-agent AI systems, transfer learning, self-supervised