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MRI scanners with massively parallel transmit and receive technologies and multinuclear options, a number of DNP hyperpolarizers, and numerous (dozens) of other scanners and instruments placed in
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Department GENERAL DESCRIPTION Conduct further research on the NSF funded project “Enabling Extremely Fine-grained Parallelism on Modern Many-core Architectures”. Benefits Our commitment to employee well-being
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and will provide support to parallel spin-off and EU projects investigating the synthesis of novel bio-based surfactants. The synthesis of promising candidates will be scaled up to the kilogram scale
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or life sciences, computer science or related field; PhD preferred Two (2) years to three years of experience in developing parallel computing applications, libraries or software tools working with
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computers (e.g., parallelizing and distributing code). Experience in distributed data management and workflow systems. Preferred Competencies Ability to build systems where agents operate with independence
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Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management
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for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel
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. The interaction between the experimental project and the parallel modelling project, focused on simulating particle generation in silicon wafers, is therefore essential. Section Mechanics of Materials You will work
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models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi-modality data analysis (e.g., image
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 3 months ago
to minimize training effort # Devise appropriate metrics to evaluate and tune trained models with respect to reproduction of key physical results # Contribute to a parallel training workflow to stream data from