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Field
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to adapt to change, Able to assemble, analyze and present data, Able to learn and retain information, Able to multitask, Able to plan, organize, and implement projects in a timely manner, Able to read
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machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual
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datasets, performing statistical analyses and machine learning approaches to identify biomarkers of treatment responses. The candidate will also develop and implement bioinformatics pipelines in a high
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Machine Learning. You can find out more about the potential content that these might include here . There will also be opportunities to contribute to the development of associated new MSc programmes in
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team. Preferential factors: academic performance, with a focus on Machine Learning and Biomedical sciences previous experience (e.g., research, professional, lecturing) in the domains of the grant
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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standards and data protection regulations. This includes managing sensitive data correctly and guaranteeing that machine learning applications are developed with ethical considerations in mind. Participate in
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-sensor hyperspectral data for crop disease detection and monitoring. Develop machine learning or physics-informed models to retrieve reliable spectral and/or biophysical features, from multi-scale
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health monitoring, preferably with a publication record in top-tier journals; and (c) be proficient in mainstream research frameworks for deep learning and computer vision. Applicants are invited
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requires a high-degree of team work and interdisciplinary activities. What You Will Do: Perform research in machine learning methods based on control theory applied to problems in neuroscience. Prepare write