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Field
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helps us question theories and improve our understanding, lending itself to better solutions. Willingness to share your insights is crucial to achieving a vibrant and creative research environment
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are increasingly being adopted across the NHS. This role is particularly suited to candidates with a strong quantitative background. We welcome applicants with training in mathematics, statistics, health economics
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PhD in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Biomedical Engineering, Mathematics, Physics, Computational Mechanics/Engineering or related discipline. (Thesis submitted by
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relevant field, including statistics, mathematics, computer science, epidemiology. Strong mathematical and quantitative skills. Experience in the implementation of mathematical or statistical models and
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machine learning, data science, mathematics or a computational science), or a postgraduate qualification with a major statistical component. There is scope for the role to be undertaken in a hybrid manner
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About us The Research Department of Structural and Molecular Biology (SMB) is a world leading academic centre that promotes multi-disciplinary research at the interface of structural, computational and chemical biology, pioneering multi-disciplinary approaches aimed to bridge across the scales...
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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expertise ranging from mathematical modelling to chemistry, molecular and cell biology, material sciences and engineering, and medical devices. The Research Fellow will be working in collaboration with
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adapt methods for electronic structure theory Manuscript preparation and presentation of results at national and international meetings. Required Knowledge, Skills, and Abilities: PhD or equivalent
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submission) in Philosophy, Applied Philosophy, Political Philosophy, Ethics, Bioethics, Feminist Theory, Law or a relevant discipline. Prior research experience e.g., as a research assistant or associate