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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
performance and ground risks in variable and uncertain geologies. The researcher will be embedded in a team that spans infrastructure resilience, computational geomechanics, and data-driven risk evaluation. The
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and engineering staff on computing architecture, including networking, data pipeline design and storage; data analysis and data collection software; and machine learning research Collaborate with other
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networks. This position is part of a UK-Canada Quantum for Science collaborative project "Quantum network applications in theory and practice" funded by STFC/EPSRC (UK) and NSERC (Canada), led by Professor
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, computation biology or a related field Preferred Experience/Skills: Experience in graphical network models, data integration. Experience with single-cell RNA seq data. Required Knowledge, Skills and Abilities
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Duke University, Computer Science Position ID: Duke -CS -PDA_RUDIN25 [#30110] Position Title: Position Location: Durham, North Carolina 27708, United States of America [map ] Subject Area: Computer
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diseases. We exploit genomic and post-genomic data to fill gaps in metabolic networks, discover new enzyme functions, and elucidate their role in disease. For more information, please visit our website
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computations, sparse tensor problems, and sparse network problems, as well as their interconnections. This position offers a unique opportunity to make significant theoretical and applied contributions
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The MRC Brain Network Dynamic Unit (BNDU, University of Oxford) is seeking a Postdoctoral Research Assistant to undertake a program of research under the supervision of Dr Ashwini Oswal (Oswal Group
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, to support our activities within the NEUROH-CHANGE project (Resilience of Neurological Health in Global Change: A One Health Approach). NEUROH-CHANGE is an interdisciplinary research program aimed at enhancing
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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer