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physics or other large-scale scientific/detector data. Experience with neuroimaging data and standard preprocessing tools. Familiarity with foundation models, transfer learning, self-supervised learning
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Primary supervisor - Dr Thomas Anthony Haynes Pellet-clad interaction (PCI) is a complicated phenomenon, relatively poorly captured in current commercial fuel performance codes. Models have been
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domains where data cannot be directly shared, such as healthcare and finance, the ability to selectively remove the influence of specific data from trained models becomes critical. Yet, despite the EU’s
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are seeking a Research Scientist specializing in Machine Learning, Data Science, Data Harmonization, and Synthetic Data Generation to lead the integration, standardization, and privacy-preserving algorithmic
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in large language models (LLMs), agentic architectures, retrieval-augmented generation (RAG), workflow orchestration and enterprise integrations, in addition to modern software engineering practices
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community of atmospheric chemists at MIT. The work will focus on interpretation of AGAGE observations of non-CO2 greenhouse and ozone-depleting gases (e.g., N2 O, CFCs, HFCs) using models, measurements and
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) Generate a web-friendly simulator of the validated regulatory model, by using SBML standard (RP2) Exploit the multiscale model to interpret the impact of synovitis on the articular cartilage, in the lights
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outages can be reproduced realistically. Validate each model independently and integrate it into the UTOPYS transmission backbone as a higher-fidelity drop-in replacement, using its standard interfaces
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technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining
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Significance & Outcomes The model demonstrates the potential to advance ovarian cancer diagnosis (up to 17 days prior to standard pathological confirmation), offering an accessible triage tool that relies