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	<title><![CDATA[Scipedia: Machine learning applications in marine engineering (ML-AMAR)]]></title>
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	<div class="panel"><div class="panel-heading"></div><div class="panel-body"><div class="row-centered"><img src="https://www.scipedia.com/serve-file/e0/l1747156008/di/c0/dG2hr_COJN1MNejnBKxsYGr5E04n4Sm4LFs1J1OIH7g/15000/19242/cover/phpZpmgTC" class="img-rounded" style="max-width: 100%;"/></div><br/><div class="institution-description"><p>The development of innovative and Artificial Intelligence-based tools for the Life Cycle Management (LCM) of ships is essential for improving the cost-effectiveness and achieving environmental goals of the new generation merchant fleets. The overall objective of the project is to develop novel LCM tools to support the new requirements of the maritime shipping industry. For this purpose, this coordinated project will gather the large experience of the two research teams (<a href="http://www.cimne.com">CIMNE </a>and <a href="http://canal.etsin.upm.es/">ETSIN-UPM</a>) in the development and application of computational methods in naval architecture and marine engineering to develop and integrate a set of Machine Learning-based tools to optimize the design, operation and maintenance of the new generation or merchant ships.</p>

<p>This project has been funded under the 2021 programme&nbsp;<a href="http://www.aei.gob.es/convocatorias/buscador-convocatorias/proyectos-generacion-conocimiento-2021">Generaci&oacute;n de Conocimiento 2021</a>&nbsp;of the <a href="https://www.aei.gob.es/">Agencia Estatal de Investigaci&oacute;n</a>&nbsp;(project&nbsp;ref. PID2021-126561OB.</p>

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