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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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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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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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alongside traditional coal fired power stations and nuclear energy generation. Revolutionary changes to power conversion is indispensable if these carbon emissions targets are to be met. The objective is to
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, allowing them to add to their existing clinical targets of anxiety and depression disorders, thus developing impact from this research. Methodology This studentship will take a mixed-methods approach to
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of senior researchers and will perform research necessary to fulfil the objectives of the Project Identification of therapeutic targets in MNX1-rearranged infant Acute Myeloid Leukaemia, externally sponsored
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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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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ultrasound. This project will develop the materials, methods, and designs necessary to 3D-print the next generation of medical micro-robots targeting drug delivery, exploiting combinations of functions
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suggest hypotheses for therapeutic targeting. This PhD studentship is part of the LifeArc ARDT, a UK-wide £12m partnership between Newcastle, Birmingham, and Belfast to accelerate rare disease trials