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-disciplinary research environment Desirable criteria 1. Experience in devising and developing novel machine learning algorithms 2. Hands on experience with ROS and physical robots 3. Excellent
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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The 6G National Research Programme is at the forefront of pioneering research and development in the field of 6G technologies. As part of the Communications Hub for Empowering Distributed Cloud
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The Oxford Agile Initiative has awarded funding for an exciting 12-month research project exploring the macroeconomic and distributional impacts of the UK’s Seventh Carbon Budget. Developed in close
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Experience with machine learning algorithms and ideally experience developing novel methods Understanding of basic biological principles and experience interpreting ‘omics data Ability to analyse information
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analysis algorithms for the observation and interpretation of existing and new spectroscopic data of exoplanet atmospheres. Experience on cloud/haze microphysics modelling and large scale simulations is
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small highly motivated inter-disciplinary team working towards a shared goal. You will be responsible for the design and testing of original machine-learning based algorithms and models for multi-modal
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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behind this efficiency by developing a general model of wing mechanosensing, revealing how sensor distribution and morphology have co-evolved with flight dynamics. The successful applicant will: Measure
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, estimation, and identification algorithms that directly interface with physical hardware. We work closely with industry partners. Our research has led to several methods now used in commercial products. We