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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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for: computers able to process information more like the brain, studying how to reproduce some of the properties of biological neurons and synapses using networks of molecules and nanostructures, and other
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stacking, both high performance and (very) high density can be achieved by this concept. The mission will be to explore, understand and develop the fundamental physics of device operation. This will require
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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and advanced material design and fabrication. Through this multidisciplinary project, the student will develop expertise in: Hands-on experience with advanced computational physics and materials
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6020, Austria [map ] Subject Area: Numerical Mathematics and Scientific Computing Appl Deadline: 2025/08/27 11:59PM (posted 2025/08/07, listed until 2025/08/27) Position Description: Apply Position
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reactive agents and biofilms, which are anticipated to play a key role in climate preparedness in water resource management. If you want to advance computational hydraulics while tackling an urgent
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: $34,002 paDeadline: 31 August 2025Start date: after 1 January 2026Candidate Requirements• A first-class Honours or Master's degree in Physics, Geophysics, Computational Physics, or a related discipline