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extending the existing VL dataset. Design and evaluate DL models capable of classifying marine litter types using multispectral data, with a focus on achieving robustness to varying spectral channel
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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of how alternative land management practices impact greenhouse-gas fluxes through the development and application of sophisticated modelling tools. The work will involve model development on the Cambridge
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associated numerical methods and AI, will be used with High Performance Computing (HPC) to improve understanding of key flow physics and inform future HPT design. Skills and Experience Required: Applicants
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combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance. The investigation will focus on optimising core gas exchange and combustion processes
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grade. Generate data through comprehensive laboratory grinding tests on various rail grades to train and validate the ML model. Utilise numerical modelling to establish acceptable thresholds for surface
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physical and numerical modelling. Feel free to reach out to the project supervisors if you have any questions. Entry requirements: The ideal applicant will be enthusiastic and self-motivated with a first
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MEng degree (or equivalent) and a PhD in Maritime Engineering and Technology or pertinent disciplines (Res Assistant if no PhD), adequate knowledge of modelling marine engines operations with alternative
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profound interest in inorganic chemistry, in both experimental and modelling applications. We are looking for candidates who are also interested in the analytical and numerical aspects of the work to support
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programming and modelling frameworks (JuMP, Pyomo, Yalmip, GAMS, etc) Experience with unbalanced distribution system analysis tools (e.g., OpenDSS, Cyme, Gridlab-D, PowerModelsDistribution.jl) Experience