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New concepts and advanced materials for quantum nanophotonics Department of Physics and Astronomy PhD Research Project Competition Funded Students Worldwide Prof A Tartakovskii Application Deadline
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fabrication, atomic force microscopy and image analysis. The successful candidate will have a 2:1 degree, or above, in either Physics, Chemistry, Materials Science or Electrical Engineering or similar subjects
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can simulate individual damage events, involving millions of atoms, with good representations of the true physics. But these simulations are expensive and cannot directly treat both the small time and
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using scans performed with adaptive current. These lead to two main objectives of this project: 1. To train deep learning algorithms to automatically segment the proximal femur from individual CT scans
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sensing technology is on the way to changing how patients are monitored and treated. By analysing extensive data from wearable devices and smartphones, you will build AI-based models that can lead to early
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available has led to growing attention in scientific disciplines. However, using purely data-driven ML approaches to modelling physical processes has many limitations such as low interpretability, out
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research, you will also harness the potential of machine learning by developing a data-driven boiling model derived from its physically-based counterpart, enabling faster and more stable CFD calculations
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on how wall shear stresses and surface temperatures affect deposit formation, and then using this knowledge to develop the optimisation process. This work can lead to a significant improvement over
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people are susceptible to loneliness but tend to be good at covering it up. Problems of loneliness, with consequences for mental and physical health, are increasingly recognised among older people
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bombardment by high energy neutrons, which smash into the materials, rearranging the atoms in our carefully engineered metallic alloys. We need to understand how this process of irradiation damage will change