37 molecular-modeling-or-molecular-dynamic-simulation PhD positions at University of Exeter
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hardware with wireless communication and complex analytical software. Mussel behaviour is not binary (open/closed) but highly dynamic, with subtle patterns indicating different stressors. Training machine
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hierarchical models and existing minimum inhibition concentration data (the lowest concentration of an antimicrobial at which microbial growth is inhibited) to refine suggested regulatory targets; Complementary
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About the ProjectProject details: Next-generation networks are rapidly outscaling the capabilities of traditional management paradigms. While early AI/ML models offered a degree of automation, they
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climate models, including the UK Earth System Model (UKESM), resulting in critical gaps in both seasonal forecasts and long-term climate projections. This PhD will develop a new parameterisation of snow
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chronic monitoring and facilitate seamless integration into dynamic neuromodulation systems for closed-loop therapeutic interventions. By leveraging graphene’s exceptional electrochemical properties with
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satellite SAR, LiDAR and/or optical imagery to enable rapid, safe, and scalable assessments of damage. Candidate methods for temporal modelling and anomalies detection, which are likely to occur at affected
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sediment–water column model we have been developing in Exeter. With a well calibrated and tested sediment/water-column model, we will then perform idealised experiments focused on a range of proposed blue
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, species distribution modelling, stomach content and dietary isotope analysis, and ecosystem modelling. Useful recruitment links: For information relating to the research project please contact the lead
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to understand the drivers and dynamics of sediment transport along this highly populated and vulnerable river. Additionally, it will explore the use of prototype water quality sensors (Hydrobeans) to understand
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statistical and data analysis frameworks are welcomed to be proposed or developed by the candidate as part of the project. We will apply the methodologies to a wide range of data from observations to modelling