122 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"https:" positions at Imperial College London
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Do you want to lead one of Imperial’s most ambitious deep-science entrepreneurship programmes? We are seeking a Programme Lead to deliver and develop Creative Destruction Lab (CDL) London, working
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As a Research Associate in Data-Driven Optimisation, you will work at the interface of chemical engineering, machine learning and automation, to develop next-generation workflows for the design and
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The Department of Surgery and Cancer at Imperial College London seeks to appoint a Clinical Associate Professor in Colorectal Surgery providing an outstanding opportunity to join a world-leading
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undergraduate, Masters and/or PhD students. You are further expected to publish findings, and help attract funding. PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering
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(Research Assistant) or PhD degree (Research Associate) in computer science or a related area or equivalent experience. Familiarity with standard machine learning libraries/data analysis, specifically as
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to combine quantitative data with qualitative and semi-quantitative information, such as local expert knowledge and citizen science observations, to improve the robustness, actionability, and local relevance
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and centres and the School of Convergence Science. Recruitment is open to expertise in all relevant areas including those with data and computational science expertise looking to both embed in medical
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contribution and personal growth. You will play a central role in advancing clinical data infrastructure, data harmonisation, and integrative data science research, particularly at the interface between data
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, model calibration, numerical analysis, data visualisation, data curation and statistical analysis. The post holder will be responsible for these outcomes and may be asked to direct team members to assist
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process and interpret simulation data, develop the implementation of optimisation techniques and modelling approaches, and help identify the most receptive forcing modes in turbulent channel flow. Working