314 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" Postdoctoral positions in United Kingdom
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Field
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Job Purpose To make a contribution to an ERC-funded project Dynamic network reconstruction of human perceptual and reward learning via multimodal data fusion, working with Prof. Marios Philiastides
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will join the world-leading team in ultrafast magnetism and THz photonics. The project is based on the recent breakthroughs: 1. Phys. Rev. Lett. 135, 246703 (2025) https://doi.org/10.1103/ldnx-67qz 2
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the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
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folding. The ability to rapidly learn new techniques is highly desirable, and enthusiasm for working across disciplinary boundaries is essential. Full training will be provided where needed. Prior
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, machine learning, or data analytics. As a proficient programmer (ideally Python), you will be curiosity-led, with exceptional communication skills, and thrive in a highly interdisciplinary environment. You
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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
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Are you interested in working on a unique geological dataset to understand the evolution of one of the most enigmatic volcanoes? Salary range £44,247 rising to £50,379 per annum Business Unit School of Natural Sciences Closing date for applicants 30-Apr-2026 Are you interested in working on a...
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. About the Role The post is funded
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in