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A four-year PhD Studentship in Pharmacometrics and Machine Learning funded by the UKRI EPSRC and GSK is available within the Institute for Global Health. The studentship will commence from 1st
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on operations. To support development of complex multi-layered systems and to inform rapid decision-making, this project will develop advanced Machine Learning (ML) architectures that learn directly from a
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computer science, with a focus on data management, distributed systems, machine learning, or related fields. Previous coursework or experience (e.g., thesis) in one of these areas is desirable. Proficiency in
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University, University of Edinburgh and Leonardo. We are pleased to invite applications for a PhD studentship to work as part of a leading team of experts. This studentship will be supported by an enhanced
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) the Computation challenge: To create new signal processing & machine learning methodologies that enable intelligent, digital & connected sensor products while mitigating the data deluge from the multiple sensors
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signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, projects in this theme will consider aspects related to: wide area motion
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an ongoing collaboration with researchers from UCL and the University of Edinburgh. For additional information, please contact Dr Xi Wang at [email protected]. When reaching out, kindly use the subject
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PhD Studentship: Molecular Simulations and Data-driven Modelling for Polymer Nanocomposite Membranes
. The application of Machine Learning (ML) to estimate the properties of polymer membranes for gas separation, including solubility, diffusivity, and permeability is gaining traction. However, significant untapped
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required to induce unstable growth, the natural frequency and oscillation dynamics under ultrasonic irradiation, and their jetting collapse dynamics. You will learn how to set up, run, and post-process large
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Summary: This research proposal addresses challenges in the UK's construction industry, focusing on sustainability & safety. It aims to develop machine learning models that combine data from similar