565 parallel-computing-numerical-methods-"https:" Postdoctoral positions in United Kingdom
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Join the Oxford Martin Programme on Forecasting Technological Change at the University of Oxford, led by Dr François Lafond, Prof J. Doyne Farmer, and Prof Max Roser. This pioneering programme aims
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methods for coating and stabilizing LFP cathodes often face limitations in durability and electrochemical efficiency. This research aims to overcome these challenges by applying innovative coating
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of Sheffield for the second year to continue this ambitious programme of research. About the project This research seeks to develop AI tools that help wargamers understand how opponents perceive and interpret
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will contribute to an exciting, interdisciplinary programme developing next-generation human in vitro models of pain. The project aims to recreate the complex multicellular interactions that underlie
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. The research will involve both analytical work and numerical computations. The balance between analytical and numerical type work is flexible and can depend on the preferences and skills of the successful
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will contribute to an exciting, interdisciplinary programme developing next-generation human in vitro models of pain. The project aims to recreate the complex multicellular interactions that underlie
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modeling. The role involves developing and implementing computational methods to integrate single-cell and spatial transcriptomics, proteomics, metabolomics, and metallomics data. Using advanced techniques
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modeling. The role involves developing and implementing computational methods to integrate single-cell and spatial transcriptomics, proteomics, metabolomics, and metallomics data. Using advanced techniques
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United Kingdom Application Deadline 30 Apr 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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geothermal processes along the volcanic arc, inform future field deployments, and serve as benchmarks for the development of new deep learning methods for volcanic seismicity. This project will apply deep