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group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
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to frailty assessment could be beneficial. Manual measurements from CT scans, however, are labor-intensive and subject to observer variability. The advent of deep learning in medical imaging presents a
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) before the human eye can see them. The principal aim of this PhD research program is to develop methods to improve the hyperspectral image classification using deep learning techniques. The developed
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The successful applicant will conduct research to design and develop novel machine/deep learning based trust technologies for securing IoT services/devices. The successful applicant will conduct
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at the intersection of causal learning, inference, and deep learning, leveraging Graph Neural Networks (GNNs) and Large Language Models (LLMs). The successful candidate will explore how GNNs can model causal structures
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are learning more and more how it relates to human health and disease. The new generation of deep metagenomic sequencing, consists in simultaneous sequencing of multiple microbial genomes at once and has
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
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they are creating. This problem is often exacerbated by the inherent bias coming from the data on which the models are often trained. Further, the wide-spread usage of deep learning models has led to increasing
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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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structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred. Application process To apply