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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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infectious disease epidemiology and mathematical modelling in Biology and Medicine. Experience in parameter estimation, knowledge of Bayesian methods and computer programming skills would be an advantage. Good
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Muscle Dynamics: Approximate muscles as “cables” with Hill model dynamics within the SOFA framework. Simulate muscle contraction patterns and their interaction with the larva’s environment, including
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to exploit approximate or computational solutions; however, this can be unsatisfactory in that it lacks in valuable physical insight. This insight is often crucial in changing partial mathematical solutions
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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to exploit approximate or computational solutions; however, this can be unsatisfactory in that it lacks in valuable physical insight. This insight is often crucial in changing partial mathematical solutions
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will be developed using the OpenFOAM toolkit. A modelling workflow will be created, and then used as the basis for the optimisation, potentially using tools such as Bayesian Optimisation. In addition
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the overall structure of the cosmic web are the various versions of the scale-space MMF/Nexus pipeline and the stochastic Bayesian Bisous method. To improve, extend and deepen the analysis to a full dynamical
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and