982 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" Fellowship positions
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based on performance and funding availability. Review of applications will begin immediately and continue until the position is filled. Qualifications • PhD Degree in Electrical and Computer Engineering
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of the Armed Forces Covenant , we ensure our hiring process is inclusive and welcoming. We are committed to ensuring reasonable adjustments are available for interviews and workplaces. Whilst all applicants will be
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to migration-related diversity. The position contributes to advancing methodological innovation through the creative and reliable use of machine learning, AI, and other algorithmic techniques in qualitative
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for real-time surveillance and precision applications. Learning Objectives: The candidate will gain hands-on experience and advanced knowledge in utilizing cutting-edge geospatial tools, including UAV-based
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invaluable hands-on experience and develop advanced skills while working closely with the Research Entomologist throughout all phases of the research process. This opportunity will allow you to learn and
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: Masters/Ph.D. degree in Electrical/Computer Engineering, specialized in Power Systems/Renewable Energy Planning/Optimization At least 5 years of professional experience with a strong focus on power system
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public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest in creative or artistic applications. Documentary evidence would be beneficial
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nowcasting platform that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes
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background in AI/NLP or speech technologies, with experience in designing and implementing machine learning models. Proficient in software development, including Python, model integration, and system
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics