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of creating robust radio frequency fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding
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fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding track record in conducting research. You
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analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate
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XRD, and the analysis of complex materials datasets · proficiency in quantitative analysis, computational methods and programming, with experience in AI, machine learning or data-driven materials
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advances in machine learning and real-time control fabricate robust instrumentation or photonic components for use at 8m-class and future ELT-class observatories perform lead roles instrument development
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: experience working with spatial omics or spatially resolved data in tissue experience applying AI or machine learning methods to digital pathology or histology data experience analysing kidney or transplant
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and/or instrument control code, leveraging advances in machine learning and real-time control fabricate robust instrumentation or photonic components for use at 8m-class and future ELT-class