14 algorithm-development-"Prof"-"Washington-University-in-St" uni jobs at University of Sheffield
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Digital twin railway network School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof D Fletcher, Prof R Harrison Application Deadline: Applications accepted all
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required
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Prof Lyudmila Mihaylova Application Deadline: Applications accepted all year round Details This research project focuses on the development of methods for intelligent wildfire detection and localisation
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across
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Computational imaging: seeing beyond the capabilities of conventional optics School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Andrew Maiden Application Deadline
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Neutrino Oscillation Study with DUNE. School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof Vitaly Kudryavtsev, Dr Rhiannon Jones Application Deadline: Applications
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Deadline: 30 June 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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electromagnetic design. We will explore advanced topologies for mmwave metasurfaces, design novel reconfiguration mechanisms, and develop intelligent algorithms to optimize scattering characteristics in real-time
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. Assess the build quality of parts generated through control model algorithms. Validate that methodologies developed are transferrable between different LPBF platforms through evaluation of parts generated