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to present your work and represent the team. You must have a PhD* in CS or Engineering (Electrical/Electronics), with specialization in one or more of the following fields: Machine Learning and embedded
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candidates must hold (or close to completing) a PhD in a relevant subject. Knowledge and experience in computer vision is required. Experience of efficient ML techniques, edge AI hardware platforms, low-power
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image processing, biometrics, security, machine learning or a related discipline, and the ability to write for publications, present research proposals and results to non-scientific audiences
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new and existing machine learning methods to build intelligent and proactive risk models. The diverse set of modelling, learning, and data management components will run on a heterogenous cloud, using
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. We welcome applications from individuals who wish to innovate research and teaching in mental health with specific expertise on machine learning and artificial intelligence methods. You will hold a
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bioinformatics, epidemiology, systems biology, large-scale ‘Omics data analysis, human centric artificial intelligence, machine learning or large data management. We are seeking candidates whose expertise spans
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generation. ECS is a progressive school with a history of pushing the boundaries of what we teach our students – and what they teach us. We value and support our staff in pursuing their research and enterprise
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generation. ECS is a progressive school with a history of pushing the boundaries of what we teach our students – and what they teach us. We value and support our staff in pursuing their research and enterprise
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generation. ECS is a progressive school with a history of pushing the boundaries of what we teach our students – and what they teach us. We value and support our staff in pursuing their research and enterprise
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progressive school with a history of pushing the boundaries of what we teach our students – and what they teach us. We value and support our staff in pursuing their research and enterprise activities within