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-Cell Genomics through the NORPOD program NORPOD is a collaborative postdoctoral program of the Nordic EMBL Partnership for Molecular Medicine . The partnership is a network of four national research
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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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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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NORPOD is a collaborative postdoctoral program of the Nordic EMBL Partnership for Molecular Medicine . The partnership is a network of four national research centers across the Nordics and the
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Fixed-term: The funds for this post are available for 9 months. Applications are invited for a Research Assistant (RA) to join the Prorok Lab in the Department of Computer Science and Technology, at
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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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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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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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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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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer