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Field
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electronics) to process information efficiently. You will work at the intersection of mathematics, physics, electrical engineering and AI, helping to develop a theory that explains how and why these systems
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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, immunology, and allergology to better understand the interaction between airway epithelium and mast cells within healthy and diseased airways, identify biomarkers, and develop algorithms for the diagnosis and
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management systems (BMS). Ability to develop and implement algorithms for modelling, estimation, or control applications. Strong analytical thinking, problem-solving ability, and capability to conduct
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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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systems with "self-diagnosis" and "self-healing" capabilities. By integrating federated learning, graph neural networks, and blockchain technology, we will develop a framework that moves beyond static
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to the academic development of the Programming area within the Programme and participate in high-quality research activities in areas related to theoretical computer science, algorithms, data structures
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Objectives - Develop hemocompatible hybrid and composite membranes for gas permeation. - Integrate Metal–Organic Frameworks (MOFs) to enhance oxygen and carbon dioxide permeability and selectivity. - Design
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algorithms for anomaly detection and predictive maintenance of subsea structures Duration: 6 months Maximum Duration Including Renewals: 12 months Objectives Development of AI-driven models to identify
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tomography and local adaptive reconstruction to overcome these challenges. Your tasks develop physics-informed, self-supervised learning approaches for phase retrieval implement reconstruction algorithms