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their career development to establish research independence. For informal enquiries about the position, please contact Prof. Keith Channon: keith.channon@cardiov.ox.ac.uk For more information about working at
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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or your viva has been held. Informal enquiries may be addressed to Prof. Tan (email: jin-chong.tan@eng.ox.ac.uk) For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us
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must hold, or be close to completion of a doctoral degree in a relevant field (e.g., data science, geography, environmental science, public health, economics). You will have experience relevant for food
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The post holder will develop computational models of learning processes in cortical networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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well as further information about the university and how to apply. This post is full time and fixed term until 1st September 2027 in the first instance. Only applications received before midday 12:00 on Monday 22nd
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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working with families, professionals and teachers to best support neurodivergent children and young people in different settings. You will lead data collection using quantitative and qualitative methods