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Field
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. Given their importance, continuous monitoring and fault diagnostics are crucial—especially as machine learning algorithms play an increasingly prominent role in predictive maintenance and reliability
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for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive
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, attention-based models, and multi-modal learning approaches—to model RNA-mediated regulatory mechanisms and their dynamic interactions in disease. The position is part of an NHMRC Ideas Grant project
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function Apply machine learning methods to identify optimal model parameters that can be used in large-scale sea ice simulations (either global parameters, or as a function of existing model variables like
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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dataset, GroupLens Research. grouplens.org [8] Statlog German Credit Data, UCI Machine Learning Repository. archive.ics.uci.edu [9] ERASER Benchmark, ERASER Project. eraserbenchmark.com [10] e-SNLI Dataset
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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: • Backend based on REST/GraphQL APIs that expose cork stopper catalogue functionalities, creation and management of final products, and consultation of machine learning model records; • Angular frontend
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modelling and machine learning to join and lead a dynamic international team of early-career researchers. This exciting role is part of a cutting-edge project investigating the structural integrity
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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques