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-flux sensors for high-pressure and temperature environments, to work within the planned programme of research. Informal enquiries may be addressed to Dr Martin Davy (email: martin.davy@eng.ox.ac.uk
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system with integrated sensors. You should hold or be near completion of a PhD/DPhil with relevant experience in the field of robotics, biomedical engineering, information engineering, electrical
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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specialist knowledge and experience in data acquisition, signal processing, and data analysis from wearable or non-wearable sensors and devices. You must be able and willing to travel away from Oxford, often
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. This can involve IoT connected devices, physical sensors or other instruments, including non-intrusive methods and inferences from a variety of data sources. You should have some experience with experimental
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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developing mathematical algorithms and simulations in MATLAB, in particular with Semidefinite Programming and Sum of Squares and of the analysis and design of feedback control systems using these approaches
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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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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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located in central Oxford. Solid-state spin photon interfaces are central to emerging quantum technologies, such as optical quantum networks and quantum sensors. Point defects in wide-bandgap solids