Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
strong analytical and problem-solving skills. A background in machine learning, data science, automation, optimisation, or control is desirable. You will have experience in analysing data with machine/deep
-
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
-
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
-
Physics informed learning for high fidelity medical simulators School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Sanja Dogramadzi Application Deadline
-
Modelling, DEM simulations and Machine Learning Approaches. You will have access to the excellent training opportunities at the University of Sheffield. A range of highly desirable skills will be learnt
-
at the University of Bristol. We seek self-motivated, innovative and creative candidates with a PhD in a relevant field (or equivalent experience) along with significant scientific experience acquired through
-
capable of predicting boiling and CHF in PWR-relevant conditions. Combining improved physical modelling with the potential of machine learning and data assimilation techniques, you will specifically target
-
/interview) A degree (Bachelor’s, Master’s, or PhD or equivalent experience) in Software Engineering, Computer Networks, Embedded Systems, AI, Data Engineering, or a related subject (assessed at: application
-
Machine Learning Methods for Autonomous Robot Navigation, Localisation and Pipe Inspection School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova
-
and monitoring data can be used to develop machine agnostic process control methodologies. Perform metallographic and non-destructive assessment of LPBF builds to assess and classify build quality