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/equilibrium reconstruction from limited, noisy diagnostic measurements. Scenario Optimization: Develop plasma scenario optimization workflows leveraging nonlinear programming, genetic algorithms, and
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experimentation. The PhD student will develop models and algorithms for the joint design of sensing, communication, inference, and action, and is expected to contribute to a real robotic platform integrating
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thermal systems, controls, or AI/ML applications Experience developing and deploying reinforcement learning algorithms for real-time control applications Experience with hardware-in-the-loop (HIL) testing
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pose estimation algorithms. The models and algorithms you develop will be part of open source and data repositories affiliated with the broader research program and TU Delft’s commitment to Open Science
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campus. We are particularly interested in researchers working in mathematical artificial intelligence, broadly construed. This includes, but is not limited to: machine learning algorithms, formal proof
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. During the PhD, you will work on topics at the intersection of probabilistic and extremal combinatorics, structural graph theory and algorithms. We study problems on discrete structures such as graphs
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-inspired, method-driven theory to application-driven research. Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms
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developing representations, using neuroimaging with fMRI and OPM-MEG, and to model the developmental process using computational models from AI. The project aims to better understand infant cognition, develop
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geometrical changes [Nat Commun. 16, 11388]. In this project you will push the boundaries of algorithm based reconstruction of nanoscale strongly scattering geometries from their radiation pattern. We seek
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(AI) and machine learning (ML) methodologies. The position involves annotating clinical data, collaborating with AI/ML/NLP teams, developing algorithms, and generating insights to improve patient care