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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (eg Operations Research, Management Science, Statistics, Machine Learning, Applied Mathematics
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Associate to join the lab of Research Professor Michael Johansson. Professor Johansson works at the interface of research and operational public health using statistical and mathematical models to improve
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of scienceYears of Research ExperienceNone Additional Information Eligibility criteria The successful candidate will hold a PhD in applied mathematics, and will have knowledge of PDE discretization methods such as
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for candidates who wish to broaden their career development. Your skills and attributes for success PhD in Mathematics (or near completion) Strong background in probability theory Ability to conduct independent
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computing environment opportunities to gather experience in statistics teaching (only if you wish to do so) We are looking for: You are holding a PhD degree in statistics, mathematics, or a similar domain
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to completion of) a PhD/DPhil in geotechnical engineering, along with experience in numerical methods, including the implementation of soil constitutive models in finite element code. You should have excellent
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econometric modeling, scientific-oriented thinking, communication and team skills, with a PhD in the field of economics, (business) mathematics, (business) informatics, marketing, management. As a member of the
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econometric modeling, scientific-oriented thinking, communication and team skills, with a PhD in the field of economics, (business) mathematics, (business) informatics, marketing, management. As a member of the
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., computational, mathematical, or mechanistic modeling) is essential. The candidate should demonstrate the ability to critically evaluate scientific literature, integrate experimental data into models, and