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, ‘Preference Elicitation and Inverse Reinforcement Learning’, in Machine Learning and Knowledge Discovery in Databases, D. Gunopulos, T. Hofmann, D. Malerba, and M. Vazirgiannis, Eds., Berlin, Heidelberg
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The University of Alabama, Department of Electrical and Computer Engineering | United States | 3 months ago
The Department of Electrical and Computer Engineering has multiple opportunities for students to embark on a fully funded Ph.D. journey within a research-intensive institution. Our students work
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of methodologies, from in-depth behavioral assessments to computer vision, machine learning and neuroimaging techniques, we aim to uncover the complexites of neurodevelopmental disorders. Our
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architectures in the field of computer vision and with training, validating and inference processes in machine learning; Familiarity with generative AI; Curious about mathematics and biology; Excellent
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technologies for medical diagnostics, treatment, and monitoring. Our research activities span computational modeling, algorithm development (using both traditional signal processing and machine learning
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multiple families), SynComs from the MICROP culture collection, and advanced phenotyping from the Netherlands Plant Eco-phenotyping Centre (NPEC). You will conduct large-scale SynCom experiments, perform
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. Simulations are suitable to characterise processes in healthy and diseased individuals including stroke patients. Machine learning methods might be considered to accelerate simulations. The project provides a
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felt along the cell population line, resulting in the first-of-its-kind living tuneable sensor with cell-specific response. Unit sensors will be robustly characterised. Data will train a machine learning
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for perceptual and creative relevance; Curate and/or utilize benchmark datasets of pareidolic visuals, and apply statistical and machine learning methods to analyze visual data and model behavior; Publish and
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid