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the power of machine learning, you will explore the interplay between metabolomic and proteomic data and depression to transform our understanding of this pervasive disorder. Based at King’s College London, a
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structural design, wave-tank experiments, energy system design, mooring design, machine learning, floating solar technologies, and previous experience of working in large international collaborations. Being
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experience of working with large databases, particularly those that contain geospatial data; experience of applying technical skills to solve real-world problems. Strong background in machine learning, data
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for Autonomous and Cyber-Physical Systems. About the Role Our reputation for leading in the field of digital systems: sensor data and signal processing for position, navigation and timing, and machine learning
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sciences, neuroscience and machine learning. About the role • Undertake the research funded by the Innovation and Technology Commission, HK• Work with colleagues in SoP and its partner institutions
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, statistical and machine learning and artificial intelligence techniques to relevant problems To disseminate the research results through publication in scientific journals and presentation at appropriate
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these projects To engage with industrial and academic partners to apply novel mathematical, statistical and machine learning and artificial intelligence techniques to relevant problems To disseminate the research
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analysis, medical image computing, and machine learning. While previous experience in these areas is advantageous, it is not essential. Familiarity with magnetic resonance imaging (MRI) is preferred
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production. About you Experience, knowledge and qualifications Relevant qualification or experience at PhD level or equivalent in Computer Science, Artificial Intelligence, and Machine Learning with a specific
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. The Research Fellow in Generative AI in Health must have a PhD in computer science, informatics, or a related discipline, as well as excellent skills in natural language processing and machine learning