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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
determination by developing mathematical methods and integrating machine learning into crystallographic workflows. The successful candidate will develop theoretical and computational approaches to improve
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technologies to enable efficient, reliable, and intelligent communication between human operators and multiple robotic systems. Furthermore, the developed methods will be translated from theory to practice
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on the candidate’s skills and interests, but may include genetically engineering microbes to change form and function, developing theory and AI methods that advance our ability to design reliable, engineered
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intelligence Develop efficient methods for maintaining reliable behaviour under changing environments, sensing conditions and resource constraints. Topics may include test-time and continual adaptation
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to become an expert in both thin films and electrochemistry – fields that are key to numerous crucial technologies and sustainability. Depending on the task at hand, you will work independently, as a part of
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for downstream applications such as mapping, simulation, analyses and other related uses. This PhD investigates methods to transform raw 3D data into structured scene representations that integrate
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quality model difficult. You will join a team that has developed a unique laser-spectroscopy method, which measures the total reactivity of pollutants using laser spectroscopy methods. In the project, this
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, modify and extend. The project will explore a range of AI predictive and generative methods, such as large language and vision models (LLMs/VLMs), inverse procedural modelling, Bayesian optimisation, world
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combination of field linguistics, acoustic analysis, experimental linguistics, computational simulation, typology, and the historical-comparative method. The project is based jointly at the University of Surrey
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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT