458 algorithm-development-"LIST"-"Washington-University-in-St" positions at University of Sheffield
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explore data-driven methods including machine learning (ML) and artificial intelligence (AI) techniques, to develop predictive HMPM tools that can diagnose, detect, and predict faults in machinery
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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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Deadline: 31 October 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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how this impacts therapeutic resistance to in order to develop more effective pan-tumour treatment strategies. This will involve investigating spatially-mediated molecular divergence in response
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opportunity for the successful applicant to develop further towards leading an independent academic career at the interface of AI and engineering applications (such as advanced manufacturing). The role holder
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Development and synthesis of zeolites for the conversion of sugars to biofuels and environmental applications Department of Chemistry PhD Research Project Self Funded Dr Marco Conte Application
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, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed
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naturally occurring electron donors or acceptors, which inhibits the chemical and biological reactions essential for natural attenuation. This interdisciplinary PhD project aims to develop a novel remediation
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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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Integration of renewables into energy systems-forecasting model development and analysis School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed