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look for you, a motivated and talented individual eager to gain experience in the sustainability industry in a defined timeframe. This internship allows you to sharpen your skillset and build a track
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with causal inference techniques such as causal graphical models, instrumental variable analysis, and counterfactual reasoning to better handle high-dimensional, multi-environment datasets typical in
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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the last five years or will be completed by December of the year of application. Have achieved or are on track to achieve an average result of H1 (First Class Honours) or H2A (Second Class Honours - A