13 mathematics-computer-science Postdoctoral research jobs at University of Oxford in United Kingdom
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for identification of discriminative spatial interactions of therapeutic response, and develop skills in computational biology and mathematical spatial analysis via independent study and training courses. It is
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via independent study and training courses. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics, computational biology, physics or a related discipline, and have experience
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, physics and astronomy, applied mathematics, statistics, computer science, etc.). The Research Associate will need to be proactive, working both independently and as part of ECI/SoGE climate community and
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, operations research, computer science, mathematical finance, or a related field, the successful candidate will demonstrate the ability to develop independent research ideas and contribute to advancing our
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experimentally investigated. About you You should possess a PhD or DPhil (or be near completion of) in the field of engineering, physics or applied mathematics together with relevant experience in the field
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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in the Mathematical Institute (https://www.maths.ox.ac.uk/groups/mathematical-biology/infectious-disease-modelling). The postdoctoral researchers will develop data-driven mathematical models and
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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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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work
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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