70 machine-learning "https:" "https:" "https:" "https:" "https:" research jobs in United Kingdom
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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms
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part of the Structural Genomics Consortium (SGC) Target 2035 Initiative, a global collaboration in the area of protein science, machine learning and data science towards improving our ability to predict
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records, aiming to co-create practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in machine learning, neuroscience
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multiple departments within the University of Cambridge as well as the collaborating organisations (RSBP, NIAB and UKCEH). The role holder will investigate machine-learning approaches that advance the core
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statistical modelling of high-dimensional data, e.g. penalised model selection and machine learning. Demonstrable understanding of RNAseq and gene expression analysis. Experience/skills handling and securely
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groundbreaking symbiosis of cutting-edge AI combined with human support. To learn more please visit https://www.kcl.ac.uk/research/embrace About the role The Research Fellow in Digital Health & Data Sciences is
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machine learning. There are currently 54 academic staff and about 105 research personnel in the Department. Please visit the website at https://www.polyu.edu.hk/ama for more information about the
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are considered advantages: prior experience with water and wastewater networks, including their operation, data, or infrastructure-related research familiarity with machine learning and statistical modelling
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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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analysis (Excel, R, Python, Prism etc). D4 Knowledge of applying artificial intelligence and/or machine learning approaches to biological image analysis or data interpretation. Experience Essential E1