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Field
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Modernising Medical Microbiology (MMM) unit at the University of Oxford (https://www.expmedndm.ox.ac.uk/mmm). You will be joining a highly interdisciplinary team of approximately 40 clinicians, computational
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emphasis on research infrastructure and technology rather than preparation for an academic career path. You will be involved in research, but more focused on learning and improving how computing, workflows
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the performance of lithium ion technologies. To support the programme, the post holder will be required to carry out research on characterisation of battery degradation, with a particular focus on the application
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
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bioinformatics/computer science will be essential. Prior experience with connectomics data is highly desirable. Our group has developed an international reputation in this area and our tools have now been used in
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About the Role You will develop and apply novel computational methods to quantify the societal impact of fundamental science discoveries. Candidates close to completion of their PhD will initially
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for the collection of clinical samples You will have a knowledge and abide by conditions of the ethical approvals obtained in relation to this programme of work To work closely with other team members and maintain
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. To discuss the post in more detail, please contact Dr Carla Solorzano-Gonzalez, Senior Programme Manager, using the contact details : carla.solorzanogonzalez@paediatrics.ox.ac.uk Only online applications
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applications. Contribute to The Foundation’s wider research and publication programme. Events, Dissemination & Community Engagement Lead the planning and delivery of academic and public-facing events, including
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene