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The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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start of employment: expected 1. August 2026 Term of employment: 4 years Hours of employment per week: 40 hours Responsibilities: Development of robust, reversible hydrogen storage systems based on metal
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- Planeamento de tratamento biológico robusto do cancro pancreático em radioterapia”, “2024.14662.PEX”, “DOI: https://doi.org/10.54499/2024.14662.PEX ”, funded by the Fundação para a Ciência e a Tecnologia, I.P
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) offer a solution by integrating physical laws with complex data. However, their effectiveness is still hindered by uncertainties related to both data and models. Strengthening their robustness requires
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external consortium partners, contributing technically and operationally to project objectives. You will translate research outcomes into robust, maintainable software artifacts, while proactively
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experienced researchers and technical staff, supporting robust scientific outputs that inform industry practice, environmental management, and policy development. What you’ll do: Undertake collation, review and
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robust, scalable software solution that integrate with subsystems such as VMware, Juniper and Fortinet network controllers, and AWS cloud native APIs. Responsibilities include maintaining and extending
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groundbreaking symbiosis of cutting-edge AI combined with human support. To learn more please visit https://www.kcl.ac.uk/research/embrace About the role The Research Fellow in Digital Health & Data Sciences is
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation