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Field
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requirements Development of data analysis frameworks. Plan, supervise and perform (if necessary) the development of supervised and unsupervised machine learning tools to process information from various data
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investigators). • Processing and analysing ICESat-2–derived wave attenuation (damping) data (2018–present) to support algorithm development and evaluation. • Integrating machine learning where appropriate
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses
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and data integration. While machine learning and computational approaches may be applied where appropriate, the core emphasis of the role is on population-level data analysis, interpretation, and
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for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
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into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging
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solver who wants to be part of a dynamic team. Information about the Church Lab: Learn more about the innovative work led by Dr. George Church here: https://churchlab.hms.harvard.edu/ , https
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades of computer science methodologies with modern data and
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collaborators. Mentor junior colleagues and students. Write, present, and publish research findings in peer-reviewed journals. Knowledge and Experience Requirements: PhD degree in statistics, computer sciences
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide