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Field
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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
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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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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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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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data collection strategy using the Internet of Things, sensors and NMEA 2000, a data analyzing plan using established guidelines and machine learning tools. We offer: A fully funded PhD position for four
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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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-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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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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should have the competencies of R3 category. Required qualification/skills/experience: - PhD degree in Electrical and Electronic Engineering or a related discipline (e.g., Telecommunications, Computer
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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