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Field
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engineering, Aalborg University is known worldwide for its high academic quality and societal impact. The Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and
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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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models using cutting-edge language technology. The project involves collaboration with experts from TU Delft, focusing on enhancing the security of machine learning model sharing without compromising data
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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of physics into machine learning and deep learning architectures to create accurate, physically consistent, efficient and interpretable/generalizable models. This PhD project will contribute to the development
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and machine learning. Master of Science (MSc) diploma in Transportation, Robotics, Computer Science, Logistics, Operations Research, Industrial Engineering, Applied Maths or any other related field
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Academically relevant background within marine control/cybernetics, computer science, or hydrodynamics, with good skills in mathematics, programming, and machine learning. Master's degree in control engineering
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, statistics, programming (e.g., R or Python), or language technology, scientific writing and analytical ability. Experience with text analysis, machine learning, or linguistic data management will be considered
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authorities. École des Ponts ParisTech, in accordance with its strategic plan, develops a long-term research activity in the field of Machine Learning and Computer Vision. The IMAGINE team is a renowned
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This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology