18 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions at The University of Manchester in United Kingdom
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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modelling and oomph-lib for continuum mechanics simulations, enabling the integration of discrete and finite element methods. Coupled with machine learning techniques, this approach will address the complex
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industries like pharmaceuticals, food processing, and construction, the project may also incorporate machine learning methods for model calibration and optimisation, driving more sustainable material handling
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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for translational biocatalysis, addressing critical needs in the development of sustainable biotechnologies. The programme will equip PhD students with advanced expertise in enzyme science, machine learning, enzyme
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applications require precision machining to achieve their final geometries. If machining conditions are not kept within specification, then damage to the material can occur, which can be detrimental to fatigue
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, stiffness loss, damage evolution, and transient creep interact under coupled loading. The project will develop temperature-dependent constitutive models informed by numerical simulation. Machine learning