104 parallel-computing-numerical-methods Postdoctoral positions at University of Oxford in United Kingdom
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and clinical neuroscience. This project involves development of machine learning methods for mapping the relationships between diffusion MRI (dMRI) and phase-sensitive OCT (PS-OCT) in the same tissue
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for chronic fuel cell monitoring in vivo including common electrochemical characterization methods. • Design and manufacture an micromachined interface between the fuel cell and the implantable
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science, with applications to public and veterinary health, therapeutic development, and pandemic preparedness. You will contribute to method development, data analysis, and collaborative projects, and will
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the model, its numerical implementation in a finite element code, and its validation against experimental data in collaboration with experimental collaborators. You will also engage with the hub activities
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and social data with the aim to better understand energy use. The programme is an interdisciplinary collaboration between the School of Geography and the Environment (Oxford), the Department
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Computational Methods for Advanced Research to Transform Biomedicine ( SMARTbiomed ), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and
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expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will focus on ambitious, ‘blue sky’ research for novel methods development
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the aim of conducting deep exploration of cancer precursors (precancers) to identify their molecular vulnerabilities and developing methods to intercept them. The alliance is directed by Professor Sarah
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mathematics or a related discipline, and possess sufficient specialist knowledge in the discipline of theoretical mechanics, mathematical modelling, and numerical simulations of PDE to work within established
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of Engineering Science (central Oxford). The post is funded by the EPSRC research funds and is fixed-term for up to one year. The successful applicant will have experience on using sequential decision-making methods