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Field
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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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(rhizotron facility) and field trials. In addition to field applications, novel inversion algorithms for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable
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two distinct images from a single PET acquisition. Within this project, we will jointly develop, adapt and implement advanced image reconstruction algorithms in our in-house reconstruction software
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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-cases of classical supercomputers, the development of quantum CFD algorithms will be of widespread benefit upon the arrival of fault-tolerant quantum computing. This project involves the adaptation
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This project develops quantum-AI tools to optimise urban hydroponic food systems and evaluate their therapeutic impact on student well-being. The expected outcome is to create quantum-reinforced AI
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of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping the technologies that underpin a more sustainable and
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students to become experts in a specific domain of choice. This vacancy is explicitly targeted at candidates interested in algorithmic biases and developing methodological approaches to tackle this challenge
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for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel
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/Qualifications Experience in: - Development of artificial intelligent algorithms. - Explanaible artificial intelligence - LLM experience - Virtual intelligence entities using reinforced learning