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advanced fundamental physical understanding of the phenomena at play but accurate predictions in realistic geometries remain difficult. You will be responsible for implementing and validating ice accretion
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advanced fundamental physical understanding of the phenomena at play but accurate predictions in realistic geometries remain difficult. You will be responsible for implementing and validating ice accretion
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of composite materials and structures. The research will be conducted within the Computational Engineering & Design Group of the Department, on a fixed term - full time contract for a duration of 20 months
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local gas/liquid phase conditions. Whilst direct simulations of breakup are possible, computational cost is high, restricting applications to small sections of geometry and for modest run times
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The Royal Veterinary College, University of London; | Potters Bar, England | United Kingdom | about 1 month ago
). This post will focus on using detailed wing geometry models and free flight kinematic measurements in computational fluid and structural dynamics simulations to recover dynamic strain and flow fields
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to work on a project investigating mechanosensing in flies (Diptera). This post will focus on using detailed wing geometry models and free flight kinematic measurements in computational fluid and structural
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Full-time: 35 hours per week Fixed-term: 24 months The Opportunity: Our team, Mechanics and Geometry of Advanced Structures Laboratory (MEGA SLab), aims to understand a broad range of topics in
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the Project PI. ii. Meeting the deliverables by carrying out the computational simulations and experimental studies planned for under the scope of work of the projects within the timeframe to meet the
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effective and efficient administrative support to the EPSRC Centre for Doctoral Training in Algebra, Geometry, and Quantum Fields (AGQ CDT) contributing to the enhancement of the student experience and
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A postdoctoral research associate position is available for a technically strong researcher to join the Oxford Machine Learning in NeuroImaging (OMNI) lab at Oxford’s Department of Computer