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rational strategies for next-generation therapeutics. The Lu Lab develops 2D/3D spatialtemporal omics, spatial pharmacology, and computational/AI approaches to map, model, and reprogram how cells
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: The candidate must have a PhD and extensive experience in modern deep neural network-based techniques. The ideal candidate should have: A PhD and a strong record of research or applied work in deep learning, with
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding
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networks for cancer metabolism, which further plays an important role in the development of novel cancer therapeutics. The Lee lab research tools include mammalian cancer cell lines, 3D organoid, xenografted
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networks for cancer metabolism, which further plays an important role in the development of novel cancer therapeutics. The Lee lab research tools include mammalian cancer cell lines, 3D organoid, xenografted
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systems in East Africa, and in the subtropics in Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We conduct
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
research project or program. Contributions will include meeting with collaborators to discuss projects, developing experimental design, wet-lab method development, sample preparation, LC-MS method
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of medicine and computer science at TUM, as well as the Munich Center for Machine Learning (MCML). It is a great place for interdisciplinary research between medicine and data science. We are looking for a Post
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Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We conduct experiments in the field to understand how
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networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning for inverse