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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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algorithms based on neural activity data (local field potentials, LFPs) from key deep brain stimulation targets including the basal ganglia and thalamus. Auxiliary data available to implanted devices include
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, particularly in computer networks, operating systems, computer architecture and distributed systems Excellent programming, system building and measurement skills are required Be familiar with, and ideally worked
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
distributed infrastructure Applications of agentic AI What You Will Get An inclusive and open-minded research environment that bridges theoretical thinking and system building, with opportunities to publish and
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framework to accommodate new components like EV chargers and distributed generation, and you will apply these insights to both large-signal and small-signal stability analyses. Working closely with academic
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). The Computer Science group is looking for students to work on one of the following projects Distributed Intelligence for Self-Organising Cloud–Edge Infrastructures Carbon-Conscious Resource Scheduling for AI Workloads
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samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy
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qualifications will be considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++) An interest in physiological
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like EV chargers and distributed generation, and you will apply these insights to both large-signal and small-signal stability analyses. Working closely with academic and industrial partners, you will
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis