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machine learning venues (e.g., NeurIPS, ICLR, CVPR) and validate research on state-of-the-art edge computing testbeds. Project description For technical reasons, you must upload a project description
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. The position The position is available within a multidisciplinary effort to develop new machine learning-based techniques for Natural Products Genome Mining at the Cluster of Excellence Controlling Microbes
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
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, and international studies—with cutting-edge data science techniques, including Earth Observation (EO) data analysis, machine learning, large-scale collation and analysis of survivor narratives
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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insights for streaming, broadcast, accessibility and media production. Candidate profile Applicants should have a background in machine learning, audio engineering, speech processing or a related discipline
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influencing factors and improve the accuracy, robustness and energy efficiency of intelligent sensing systems. Apply AI as an engineering tool: Use signal processing, statistical methods and machine learning
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systems, or continuous-time and discrete-time LTI systems theory is a plus. Experience with mathematical modeling, optimization, numerical computation, algorithm development, or machine learning. Prior
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, physiology and disease development; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will