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an emphasis on the development of methodologies and techniques for Evolutionary Computation and Machine Learning. Work plan: Review of the state of the art in Machine Learning and Deep Reinforcement Learning
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the state-of-the-art wind tunnel facilities of the Department of Aeronautics, and will utilize novel theoretical and machine learning tools. You can expect to become an expert in aerodynamics and turbulent
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machine learning or trustworthy AI, including experience with robustness assessment and attack/defense mechanisms. Expertise in software security and code analysis, with understanding of common
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develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning
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series analyses, (2) Earth or planetary remote sensing, (3) Data science approaches, including statistical methods, handling of large datasets, pipeline development and/or machine learning (4) Full stack
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This project fuses machine learning (ML) based inverse design approaches and topology optimisation (TO) to realise multiscale
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Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references A max. two-page writing sample on your views regarding research into social
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, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) Strong skills in machine learning and deep learning Experience with modern NLP methods, including transformer models
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, machine learning, or (astro-)physics (in particular cosmology, galaxy formation, or general relativity) will be an advantage. What we offer: Inspiring working atmosphere: You will have the opportunity
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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar