325 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof"-"Prof" positions at University of Sheffield in United Kingdom
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Development of a Lattice-Boltzmann based model for the deposition process in chemically reacting flows School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof
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Development of a novel material model for machining prediction School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Hassan Ghadbeigi Application Deadline
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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
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Development of biofidelic test-beds for assessing human interactions School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof M Carre, Prof R Lewis, Dr J Rongong
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of Sheffield and Nottingham, and the Alan Turing Institute. The wider focus of this research programme is to develop both physics-based and data-driven models of heart function and blood flow through
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Overview The Research Hub Manager plays a pivotal role in driving research success by providing comprehensive support for grant submissions and proposal development within an academic setting
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Overview We have an exciting opportunity for a motivated researcher educated to PhD level (or close to completion, or with equivalent experience) to contribute to the development of a system based
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the running and development of the University’s maintenance management system, attending training courses for updates on the system. · Assist in the running of the Planned Preventative Maintenance
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professional development opportunities and CPD opportunities to develop your skills and experience, with an employer which values and promotes equality, diversity and inclusion. Main duties and responsibilities
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adapted based on the abilities and needs of patients. Moreover, automatic intelligent algorithms will be developed in to make the control intuitive, natural and adaptive. Such that the model can learn new