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project team regularly use for the production of model colloidal films, ceramic dielectrics, photovoltaics and battery electrodes to provide the datasets required to educate the machine learning algorithms
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oversee and develop algorithms for analyzing ensemble genomics data, single cell genomics data, single cell merFISH and sequential oligopaints imaging data, as well as novel molecular connectomics data
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candidates will have specialist knowledge in signal processing and algorithm design, with experience in machine learning, AI system development and reinforcement learning along with a strong publication record
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. Learn more about Sandia at: http://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: Sandia provides systems, science, and technology solutions to meet national
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turbine blades. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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Doctoral Candidates (DC1 and DC2) to carry out research in neuromorphic photonic-electronic integrated circuits for brain-inspired information processing and sensing (DC1) and in the development of efficient
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co