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research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization. Some knowledge of probability and
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are seeking a Research Fellow to lead the development and
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, numerate subject (such as maths and physics), including Matlab programming. Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse/research-degrees/applyphd
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: (i) the rarity of extreme events, which renders classical statistics inadequate; (ii) the uncertainty inherent in cascading effects; and (iii) the lack of confrontation between numerical models and
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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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the development of numerical models. Metrics to quantify damage detection effectiveness, such as Probability of Detection (POD) and Model-Assisted POD (MAPOD), will be identified to develop a figure of merit
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differential equations, spectral analysis, and scientific computing techniques. Proficiency in programming (MATLAB, Python, or equivalent) is required for developing and implementing numerical models. Experience
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Institut Supérieur de l'Aéronautique et de l'Espace | Toulouse, Midi Pyrenees | France | 17 days ago
, 'numerical' tests will be carried out on laboratory structures. The second challenge will be to extend the model in order to deal with multi-cracking (an important point will be how to manage the ramifications
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microfluidic devices used as proxies for the vaginal microbiome. The project will involve first principles mathematical modelling, analytical and/or numerical solution of simplified PDEs for flow physics and
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) invites applications for a fully funded 4-year PhD program in Process Systems Engineering. The project aims at integrating data-driven optimisation and design of experiments techniques with model-based