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the device and for algorithm efficiency as compared to qubits. We will explore the use of tightly focused laser beams and their interaction with crystals of trapped ions to realize new ways to prepare and
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, privacy, and resilience. Today’s Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable
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graph learning models, primarily geared towards assisting combinatorial solvers for practical graph algorithm benchmarks. Please find out more here: Dr. G. Rattan Your profile You have, or will shortly
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solutions, including training algorithms and preparing solutions for clinical implementation. Assess the impact of your workflow solutions after implementation, determining whether the expected improvements
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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for problems that are 'quantum' NP-hard (QMA-hard). What you will do Quantum algorithms and complexity theory; Quantum error correction protocols; Quantum information theory; Classical representation of quantum
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order to be successful, you bring: MSC in Computer Science, Physics, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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participants of the Netherlands Twin Register, integrating genetic and psychological data where relevant. Beyond algorithm development, you will also address methodological challenges such as data quality, bias