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approaches to non-invasively localise ventricular arrhythmias, collectively characterised by the integration of patient imaging and electrical data, with state-of-the-art in-silico modelling and AI-algorithms
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mechanism design, algorithmic game theory, or social choice theory, as evidenced by peer reviewed publications 3. Strong mathematical and analytical skills 4. Excellent written and oral
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CANDIDATES ONLY About Us The applicant will join the Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. We are a highly collaborative
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