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Field
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. CBQB is committed to lead major discoveries in disease mechanisms, novel diagnostics and therapeutics, as well as development of devices and algorithms that will improve health, with lasting impact to
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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to achieve the following objectives: 1. Characterize 3-D Urban Structure and Change: Utilize data from multiple remote-sensing platforms and deep learning algorithms to generate high-resolution maps of 3-D
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Alexandria, Virginia. The focus of these positions will be on quantum computing, quantum algorithms, quantum learning, quantum error correction, and quantum fault-tolerance. The successful candidate will join
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University, to begin as early as July 1, 2025. Topics include the experimental quantum simulation of chemical and condensed-matter systems using 1D and 2D ion arrays, and the development and optimization
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machine learning, statistics, or applied mathematics that could drive the frontier of biomedical research. The role will be focused on the development of novel computational and algorithmic methods, with a
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a multidisciplinary research team focused on developing energy-efficient and fault-tolerant AI systems that can operate reliably in the radiation-rich environment of space. The project integrates
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, Bioimaging Sciences Position Description: Join an exciting effort to develop a low-field, low-cost, MRI scanner for screening mammography. You will participate in the development of MRI reconstruction
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Qualifications - Experience in developing algorithms for analysis of biological data. - Experience with single cell and spatial transcriptome data analysis. - Experience in supervised and unsupervised machine
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will have the opportunity to develop innovative algorithms and models that integrate multiple data modalities, collaborate with industry partners, and contribute to high-impact publications. Job