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Field
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, engineered powders, including Cermetal and WC-Co-based materials, will be investigated as energy-absorbing media within the damping system. The development combines computational modeling, machine learning
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selection criteria Knowledge/experience with control engineering, information fusion and/or data assimilation, marine technology Knowledge of and hands-on experience with machine learning and/or statistical
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Start date – 1st March 2027 (latest) Project Description Machining generates large quantities of swarf, with many components losing 60–90% of their material during the machining stage. This swarf
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problems > Experience with modern AI techniques and methods or desire to work on Applied Machine Learning Problems > Constantly questions finance/trading data and stays motivated to seek answers despite most
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. Government defensive missions. Our team provides technical guidance in capability and capacity development for Security Operations Centers (SOCs), National Cyber Centers, and Computer Security Incident
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defence of a PhD thesis in the field of Electronics-ICT: Artificial Intelligence. This PhD project focuses on the development of intelligent control algorithms for inland waterway vessels, using machine
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, machine-learned interatomic potentials, molecular dynamics, kinetic Monte Carlo modelling and comparison with experimental data from the Faraday Institution FAST programme. Faraday Institution PhD students
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-cell imaging, multi-omics profiling such as transcriptomics, proteomics, and metabolomics, single-cell and spatial analyses, multiplex biomarker quantification, functional studies, machine learning
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. The supervision team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation
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candidate for a full-time (100%) PhD position for 3 years. You will join the research group Power Electronics and Electrical Machines (PEM) at IEL, where we foster an open, inclusive, and collaborative