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Field
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is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup, integrated with Machine Learning algorithm, as a high throughput method for screening and developing formulated products that are used in
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machine learning algorithms, so that the resulting AI model can run on a variety of devices, including UAVs (e.g. drones) that may be used in turbine inspection. The overall aim is the design of a portable
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Location: United Kingdom (Remote or Belfast, NI Office based) Position Overview We are seeking a Senior Machine Learning Researcher with deep expertise in computer vision and hands-on experience
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operating point and environmental turbulent flow conditions. Understanding of all the former conditions is critical to inform industry about the performance of tidal turbines and to develop a machine learning
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quantum chemistry (QM), machine learning (ML), and high-throughput experimentation (HTE). The objective is to develop a data-driven framework that enhances the efficiency and effectiveness of catalyst
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oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
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PhD studentship in Machine Learning for Computational Physics and Chemistry, University College London, UK A 3.5-year PhD studentship is available to work under the supervision of Prof Jochen
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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tools such as LTSpice (preferred)/TINA/Multisim. Knowledge of low-power biopotential amplifier design and energy harvesting techniques is preferred. Candidates with machine learning skills, particularly
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in