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Field
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deployable Machine Learning (ML) solutions integrated into Industrial Internet of Things (IoT) edge devices for condition monitoring. While ML-driven industrial condition monitoring offers significant
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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Academies of Science Engineering and Medicine Workshops. Selected candidates will have the opportunity to train for publishing in leading biomedical journals and machine learning conferences, networking with
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PhD Project: 3D-printing next-generation electro-actuators for soft robots and devices Applications are invited for a PhD project within the Faculty of Engineering, in the Centre for Additive
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interaction and human motion analysis Prior knowledge of machine learning/deep learning applied to motion analysis (e.g., relevant courses and research experience) would be an advantage IELTS score of 6.5
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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pharmaceutical manufacturing and additive manufacturing. Requirements: Candidates should have a background in one of: engineering, materials science, chemistry, and be willing to learn new disciplines and innovate