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Deadline: 20 June 2025 Details R2T2 is a UKSA-funded doctoral training programme dedicated to academic research in rocket propulsion, with an emphasis on the acquisition of the practical skills required
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subsystems • High performance tunable and reconfigurable oscillators and frequency synthesisers • Application of AI / Machine Learning to physical layer circuitry, signals and waveforms Researchers can expect
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the past has been to exploit approximate or computational solutions; however, this can be unsatisfactory in that it lacks in valuable physical insight. This insight is often crucial in changing partial
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effective control strategies is vital to typhoid elimination efforts. Typhoid toxin damages human cells causing an ageing-like process A major virulence factor implicated in typhoid is the typhoid toxin
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that operate with minimal computing, sensing, and actuating resources—essential features for implementation in real-world scenarios. To this end, we will leverage sophisticated mathematical tools such as
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A Machine Learning Enabled Physical Layer for 6G Radio Systems
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://www.sheffield.ac.uk/mac/research/groups/rise For informal enquiries about this job contact Abigail Hathway, Project Lead on a.hathway@sheffield.ac.uk Next steps in the recruitment process It is anticipated
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) Our website www.sheffield.ac.uk/social-sciences For informal enquiries about this job contact Daniel Rose, Faculty Research Manager: on daniel.rose@sheffield.ac.uk Next steps in the recruitment process
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Interplay between Algorithms and Combinatorics School of Computer Science PhD Research Project Directly Funded Students Worldwide Prof Parinya Chalermsook Application Deadline: 31 July 2025 Details
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noise, and communication delays. The challenge lies in ensuring the desired behaviour of an infinite-dimensional system with limited computing, sensing, and actuating resources. To tackle