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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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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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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
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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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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
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» Algorithms Mathematics » Computational mathematics Mathematics » Mathematical logic Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 16 Feb 2026 - 22:59 (UTC) Type
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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, integrated circuit designers, and quantum algorithm developers Analyze and interpret experimental data, contributing to scientific publications, patents, and presentations. Engage with the wider international
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework