304 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" Postdoctoral positions in United Kingdom
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experience in cosmological simulations, analysis of cosmic microwave background and/or large-scale structure datasets, machine learning methods applied to cosmology, or theoretical modelling of cosmological
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CANDIDATES ONLY About Us The applicant will join the Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. We are a highly collaborative
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in theory of probability and statistics, machine learning, or formal methods. The post is available from 2 March 2026 until 1 March 2028. If you are still awaiting your PhD to be awarded you will be
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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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frameworks for quantum machine learning, including conformal quantum prediction and uncertainty quantification in quantum models; Theoretical and algorithmic advances rooted in statistical learning theory
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), working closely with Professor Catrin Tudur Smith and Dr Ahmed Waqas. The Postdoctoral Researcher will work on a network meta-analysis of psychosocial interventions for military personnel with Posttraumatic
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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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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