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Main description: Native mass spectrometry is an expanding structural biology tool to elucidate the function of protein complexes. Excitingly, the demand for this technology is increasing. However
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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Theoretical models for gravitational wave signals emitted by coalescing compact binaries are the cornerstone of modern gravitational wave astrophysics. Among the most pressing challenges
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our ability to predictably control and exploit the drop for useful tasks. The proposed project has two aims: First, to develop computational models to quantitatively predict the response of chemically
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existing biomedical foundation models (e.g., Med-PaLM) using techniques like Low-Rank Adaptation (LoRA). Big Data Analytics: Managing and analysing complex, multi-modal data from globally significant
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the corrosion of reinforcing steel, which compromises safety, durability, and sustainability. Current corrosion prediction models often fall short because they rely on oversimplified assumptions and
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the necessary theoretical tools, using cavity quantum electrodynamic (QED) descriptions, to model and understand this complex interaction between plasmons and molecules, to reveal the necessary procedures
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tools using cavity quantum electrodynamic (QED) descriptions, to model and understand this complex interaction between plasmons in small gaps and the vibrational behaviour of molecules. The student will
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semiconductor devices, large antenna arrays for satellite communication, and advanced electromagnetic surfaces with unconventional material properties. As these devices continue to become more complex and operate
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collaboration with modelling or industrial partners Candidate Requirements We welcome applications from candidates with the following background: Academic degree (BSc / MSc or equivalent) in Materials Science