138 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in United Kingdom
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well as companies and governmental organisations . They will contribute to the activities of the wider machine learning and data science research group and write up the results of their work, with co-authors
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collaborate with other technical groups working on the design. The successful candidate will also have opportunity to conduct experiments and machine development activities on the existing accelerators. The key
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: Experience implementing Quantum Monte Carlo methods. Experience applying Machine Learning methods to scientific problems. About the School The School of Physical and Chemical Sciences is one of the UK’s elite
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manufacturer. Based on our publication (ACS Appl Nano Matter 2022, 10.1021/acsanm.2c03406) and our ongoing collaborative work, we have developed a new chemical assay coupled with a machine learning algorithm to
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tablet based cognitive screening tools for use with those with acquired brain injuries (e.g., stroke, traumatic brain injury etc). The post-holder will be expected to be central in data collection in
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conferences in machine learning, statistics, and communications. Presenting research findings at project meetings, workshops, and international conferences. Supporting the supervision of PhD students and
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the scientific investigation of artworks and historical objects. The project aims to advance the mathematical foundations of imaging and machine learning while directly supporting research in art history
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About us: We are seeking to appoint a postdoctoral research associate with an excellent track record in semantic technologies and machine learning. Topics of interest in this area include, but
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tablet based cognitive screening tools for use with those with acquired brain injuries (e.g., stroke, traumatic brain injury etc). The post-holder will be expected to be central in data collection in
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ecology that challenges Western narratives and explores alternative ways of thinking and living with weeds by learning from traditional knowledges in their places of origin; a multispecies geography that