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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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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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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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centres. At Cambridge, our mission is to contribute to society through world-class education, learning, and research. With a deep respect for tradition and a bold vision for the future, we continue to shape
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a useful reconstructed image by employing a suite of AI/machine learning tools with limited impact on the task dependence. Specifically, the successful candidate will develop models and methodologies
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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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environments Enjoys helping others learn and grow Is confident using lab or workshop equipment like cement mixers, hydraulic presses, and testing machines Has worked with materials like concrete, soil, or timber
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through various channels, including email, one-on-one meetings, and an online studio space. You will work closely with the course and module leaders to enhance the student learning experience. The role
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We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the Northeast
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, engage with research that will inform teaching, and actively support recruitment and outreach activities, such as open day and school liaison events. You will teach across our BSc Geography and BSc