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candidate to join the UKRI Future Leaders Fellowship (FLF) research project of Dr. Zsuzsanna Koczor-Benda on “Quantum embedding for functional nanodevice design” in the Department of Chemistry at
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Electronics, will use computational simulations to study how thin films form during flowable chemical vapor deposition (FCVD), a process used to build advanced semiconductor devices. Unlike traditional CVD
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, including in liquids. Combining quantum mechanics and atomic simulation with AI-driven sampling techniques, you will determine terahertz and Raman spectrograms to directly compare to measurements obtained in
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resolution. By combining quantum-mechanical modelling, tomographic reconstruction, and data-science methods, the project will reveal how skyrmions interact with defects, helping to design the next generation
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rigorous theoretical framework to interpret the measurements is still lacking. This project addresses this gap by combining quantum mechanical calculations with continuum micromagnetic theory to bridge
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backgrounds in engineering, applied mathematics, physics, computer science, or data science, and can be tailored to your interests, whether you prefer theory, modelling, or applied research. You will analyse
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to obtain) a 1st/2.1 or Master’s degree in Engineering, Physical Sciences, Life Sciences, Data Science, Mathematics, or a related field. Strong quantitative and programming skills are required. Interest in
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physics-based and data-driven methods to support the design and scale-up of these systems. This approach will reduce the need for costly experiments, improve scale-up predictions, and provide confidence
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) and long-term properties (including creep and fatigue behaviour) will be analysed. Furthermore, the project will develop physics-assisted artificial intelligence (AI) models that integrate experimental
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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
process by which proteins traverse the free energy landscape, thus connecting the unfolded and the folded (native) state. Understanding protein folding mechanisms is a key route to better understanding