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machine learning approaches show issues in model performance and efficiency and vulnerability towards the application of noise over a large number of distributed models. These issues should be overcome by
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analysis and data processing. Strong programming skills in R (preferable) and/or Python, and experience or interest in weather prediction or climate models. Knowledge of machine learning, AI techniques, and
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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Building tools to detect or prevent unsafe AI outputs Exploring regulatory gaps and proposing solutions This is an ideal opportunity for candidates with interests in machine learning, public health, ethics
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degree with strong skills in programming and machine learning. Please contact Zhuang Li for more information. The project focuses on developing multilingual datasets and advanced methods to detect and
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supervisory team from La Trobe University and Sheffield Hallam University, gaining expertise in data-driven performance analysis, statistical modelling and machine learning. This research will enhance our
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machine learning (CML) to enhance the predictive accuracy of genomic prediction models by uncovering the underlying cause-and-effect relationships that drive trait variation. CML integrates machine learning
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instance learning and weak supervision / spatial transcriptomics models to individualise tumour type, associated biomarkers and genomic characteristics to high precision. The resulting multipurpose machine
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models for structural health monitoring of civil engineering structures. Digital twin models are used to interpret real time information from videos and images aided by computer vision techniques