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biology/bioinformatics, statistics, machine learning or related field. You will have a strong track record of applying genetics-based, physicochemistry-based and structure-based computational or statistical
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computing systems design and realization, including machine learning (ML) and artificial intelligence (AI) applications including autonomy, sensing and communication, advanced manufacturing, and decision
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Candidates MD, DO, MBBS, PhD, EdD, or equivalent in a related field such as the health sciences, education or other field given context of work experience and/or other qualifications. Qualified for a faculty
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Shifting the paradigm: machine-assisted scholarly digital editing Digital Humanities Institute PhD Research Project Self Funded Dr Isabella Magni Application Deadline: Applications accepted all year
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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to teach in at least one of the thematic areas listed above. Additional desired qualifications include a PhD degree, the ability to teach across multiple areas of the curriculum, strong engagement with
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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who could help with general course delivery, project supervision and marking of assessments. The post holder will teach undergraduate and computing modules within the School of Computing and Engineering
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action recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning