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participants Ideally, practical skills in one of (a) programming, (b) machine learning, and/or (c) design Responsibilities Developing and conducting novel research projects individually and on teams Developing a
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be called upon to provide instruction for safe operation of conventional lathes and milling machines in our student workshop. You will also provide technical advice, instruction and support to academic
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The Role Join our growing team to help re-architect and re-implement the University’s research virtual machine (RVM) and high-throughput computing (HTC) platforms. Beyond provisioning and
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will play a leading role in the analysis of large and complex genetic and electronic health records datasets with a range of Statistical and Machine Learning approaches, whilst leading a broad range of
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confirmed Reference: 0482-25 We are seeking to recruit a Research Associate or Senior Research Associate, to conduct cutting edge research covering digital twins, AI/machine learning data analytics
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of innovative particle identification and noise filtering algorithms based on Machine Learning techniques to improve the detection of muons in a variety of particle detector systems. 3. Lead the development
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will have experience with working in a machining environment, including turning and milling and a have strong ability to teach modules relating to other areas of engineering. If you are not a qualified
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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applications. Applications are sought from across a diverse set of disciplines under the Information Engineering umbrella, including, for example, physics-informed machine learning and AI, computer vision
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national and international research networks. We are looking for someone with a PhD (or near completion) in a relevant field such as machine learning, biomedical engineering, or neuroscience, with expertise