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flow conditions. Here, thermodynamic consistency refers to a modelling framework in which the coupling between processes of different physical nature is derived from fundamental physical principles
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conditions. Here, thermodynamic consistency refers to a modelling framework in which the coupling between processes of different physical nature is derived from fundamental physical principles, ensuring
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theory and framework to study and explain how different reservoir systems work and how to design them for specific tasks. The project will combine: Mathematical modelling of dynamical systems
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science or systems engineering. Knowledge of AI/ML algorithms, particularly graph neural networks and reinforcement learning, is highly advantageous. A keen interest in distributed computing, IoT architecture, and
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a focus. Traditionally, this is done through iterative algorithms (‘trial and error’). In this project, we aim to develop a radically different approach where the correct shape is computed using a 3-D
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at the interface of machine learning, statistics, probability, and with applications in statistical genetics, developing new theory, algorithms, and scalable implementations. Starting date as soon as
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that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase retrieval in the weak holographic regime
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. Therefore, we need an imaging scheme that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim