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infrastructure. The research will investigate how machine learning models can be designed and deployed efficiently on constrained hardware platforms while supporting the reliability and security requirements
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. You will gain expertise in experimental acoustics, advanced numerical modelling, sustainable building technologies and performance optimization. Are you motivated to take a step towards a doctorate and
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of Aeronautics, collaborating with experts in DNS/LES, dynamical systems, and data‑driven modelling. Hands‑on experience with high‑performance computing, advanced numerical methods, and ensemble‑based optimisation
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) and the development of advanced numerical models considering the thermo-mechanical behaviour of structural elements. 1. Applicable Legislation: This tender, open by Decree of 29/12/2025, by the Rector
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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numerical modelling to investigate vapour-liquid-solid phase transitions in aircraft exhaust plumes under controlled conditions, with particular relevance to emerging low-soot propulsion technologies such as
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the scientific field of the competition. c) Previous experience in statistical analysis of time series and mixed models. d) Previous experience in processing and analysing physiological data (electrodermal
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-based workflow including Spatial GSA and statistical methodsEnhance monitoring accuracy and validate numerical models using field data The Doctoral researcher will be supervised by Prof. Lavasan, and will
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geotechnics/soil mechanics. The following desirable skills are an advantage, but not a requirement. Data analysis skills in python An interest in energy policy / the economics of energy Numerical modelling