35 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" PhD positions at University of Nottingham in United Kingdom
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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applicants who have a background or strong interest in Computer Science, interactive media, software engineering, 3D modelling/animation, VR/AR, human–computer interaction or related digital-tech fields
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow
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This exciting opportunity is based within the Power Electronics and Machines Centre (PEMC) Research Group at Faculty of Engineering which conducts cutting edge research into enabling technologies
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art