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infrastructure The project is embedded in the Team Policy, Politics and Society of the Department of Public Administration and Sociology at the Erasmus School of Social and Behavioural Sciences of the Erasmus
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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that support sustainable land management and policy decisions. About you We are seeking a candidate with a strong background in mathematical biology, ecological modelling, or biostatistics and a strong interest
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proteins from renewable feedstocks and waste streams. We aim to develop sustainable processes to convert these materials into high-performance fibers and functional materials. By manipulating molecular
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to their ability to perform the proposed project within the allotted time. The candidates are evaluated by a Selection Board, which ranks the candidates based on the criteria indicated above and conducts interviews
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profoundly affect their mechanical properties and overall performance. Therefore, understanding the temperature field and developing effective thermal control techniques are vital to ensuring a high-quality WA
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. immunohistochemistry, flow cytometry, high-performance liquid chromatography, real-time PCR) Preparation of scientific publications and presentations at conferences and in expert committees in conjunction with business
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recommended. Practical experience with Linux/Unix, High Performance Computing, and scientific programming is recommended. Basic expertise with scientific data analysis. Good skills in scientific writing. We
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and empathic behaviours most effectively improve patient outcomes. In this role, you'll conduct NMA, perform data analysis, and contribute to research output. Your work will inform future NIHR funding
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) to engineers and automated systems •Validate the system’s resilience, scalability, and practical relevance using real-world and representative datasets, with evaluation of technical performance and potential