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Field
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A Research Fellow position is available in the group of Professor M. J. Rosseinsky OBE FRS to work in a team of computer scientists and materials chemists funded by the AlChemy. AI in Chemistry Hub
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analyses of these data for research, quality improvement and surveillance purposes. The incumbent will apply appropriate methods to aid in the creation of a learning health system around opioid-related
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analyses of these data for research, quality improvement and surveillance purposes. The incumbent will apply appropriate methods to aid in the creation of a learning health system around opioid-related
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) umbrella, using artificial intelligence, signal and image processing, computer vision and statistical methods for medical applications – all in collaboration with user partners from hospitals and
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in Community Connectedness. This position will play a key role in developing the data, methods and evidence on community participation in the UK, working across the Centre’s activities. The Centre’s
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undertaking a specified range of activities within the research fields of holography, gauge theory and related areas. Role Summary Work within specified research grants and projects and contribute to writing
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the analysis and processing of fine and microscale oceanographic data, preferably from autonomous platforms. We value knowledge of inverse methods to estimate ocean circulation and mixing. You will demonstrate a
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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering
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applications for a postdoctoral position within the research project “Statistical Methods for Online Detection of Anomalies” (SODA), newly funded by the Norwegian Research Council and affiliated with Integreat
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/ developing research profile and/ or respected national/ international profile in mixed methods around human-computer interaction PhD or equivalent in relevant subject area or the equivalent in professional