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X-ORIGINAL-URL:https://cmc.deusto.eus/
X-WR-CALNAME:cmc.deusto.eus
X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
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UID:MEC-948ba1dc8cc4cc26e5d9d4f358660c2d@cmc.deusto.eus
DTSTART:20211028T060000Z
DTEND:20211111T120000Z
DTSTAMP:20211105T161700Z
CREATED:20211105
LAST-MODIFIED:20220117
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:Open call Predoctoral position: Kinetic Equations and Learning Control
DESCRIPTION:Open call Pre doctoral position at Retos project\nProject: Retos – Kinetic Equations and Learning Control (PID2020-112617GB-C22)\nSupported/funded by AEI – Agencia Estatal de Investigación (Spain).\nLocation: Chair in Computational Mathematics at Deusto Foundation/University of Deusto, Bilbao – Basque Country (Spain)\nSubmission process: OPEN\nDuration: 3 years\nDeadline: November 11st, 2021 14:00H  (Madrid/Europe)\nSupported by AEI – Agencia Estatal de Investigación (Spain), the Chair in Computational Mathematics is looking for a predoctoral researcher to develop a PhD thesis in a high quality research environment and training through the Retos project at Deusto Foundation/University of Deusto, Bilbao – Basque Country (Spain).\nMachine Learning (ML) and data science more generally are revolutionizing applied mathematics, leading to rich, intensive and innovative research and a variety of powerful new ideas and methods. We seek to contribute to its mathematical foundations, paying special attention to the challenges in the area of ​​dynamic systems control, where the classical model-based paradigm must be complemented with a series of data-based techniques.\nOur goal is to foster rigor in the analytical and computational foundations of emerging blended data / model-based control methodologies, building a specific and productive bridge between data science and applied mathematics, and generating novel and advanced hybrid algorithms and approaches. performance. In this context, great advances are being made in the broad field of control engineering, strongly influenced by ML. This makes it possible to tackle a growing number of challenging applications, but the theoretical and analytical foundations are still lacking in terms of rigor. Data-driven approaches provide a new range of powerful tools for designing superior control strategies. Similarly, some of the key concepts and results of the control shed light on some of the main challenges in architectures for neural networks, for example. But the theoretical pillars are weak.\nThe goal of this subproject is to address key analytical and computational issues that are not well understood or remain unsolved, but that appear across the full range of real-life and technology applications (aeronautics, autonomous driving, resource management, epidemiology, robotics , biomedicine, internet, etc.) and require a unified treatment.\nDetailed information about this call\nSend your submission\nThe application process is conducted via the online platform of the MINECO (Ministerio de Ciencia e Innovación). Please, read all the information in the Guide for Applicants before submission.\nDeadline: November 11st, 2021 14:00H  (Madrid/Europe)\nGuide for Applicants\nDetailed information about this call\nCheck this on our calendar\n
URL:https://cmc.deusto.eus/events-calendar/open-call-predoctoral-position-kinetic-equations-and-learning-control/
ORGANIZER;CN=DeustoCCM - Chair of Computational Mathematics:MAILTO:
CATEGORIES:Events Calendar,Events Calendar Past
LOCATION:Unibertsitate Etorb., 24, 48007 Bilbao, Bizkaia
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/2021/11/DeustoCCM-jobsBoard-retos-predoc-2021.png
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