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to develop an aeromedical dispatch management software as a technology hub that provides data-driven prediction model and an automated dynamic decision model. The successful candidate will be responsible
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engineering Machine learning or AI methods (e.g. anomaly detection, classification, regression, time-series modelling) Programming skills (e.g. Python, MATLAB or similar) Experience with industrial systems
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industrial decarbonisation modelling to support the EU-funded FLARE project. The role will lead the technical development and integration of bottom-up, organisation-level decarbonisation models for energy
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modelling, data-driven approaches, and experimental validation. The project combines computational materials science with practical membrane design for electrochemical energy applications. PhD Position 1
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and intelligent systems for industrial applications. Based within the School of Engineering and Architecture, IERG develops and applies cutting-edge analytics, modelling, and decision-support
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SPEAR Centre: PhD in ‘Long-Range, High Bandwidth Distributed Acoustic Sensing for Fibre Optic Links’
-border project by Atlantic Technological University, Ulster University, Tyndall National Institute and their associate partner Seagate Technology. The Centre aims to build research capacity in integrated
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methods, leveraging digital twins, data analytics, and reliability engineering. In parallel, the role includes project oversight responsibilities, supporting effective coordination of consortium activities
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energy efficiency and intelligent systems for industrial applications. Based within the School of Engineering and Architecture, IERG develops and applies cutting-edge analytics, modelling, and decision
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) Position C Powering data-driven sustainability assessment tasks in agri-food systems with IoT-data Datlakes and Large Language Models. Aarhus University (DK) & Université Libre Bruxelles (BE) Position D
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-heavy model updates, the proposed approach will use event-driven and sparse-update mechanisms so that learning updates are transmitted only when meaningful local changes occur. This will significantly