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to surveillance of infectious pathogens using computer science and mathematics? Join the Delft Bioinformatics Lab and work on graph-based algorithms for microbial genomics! Job description Bacterial and viral
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Do you want to contribute to surveillance of infectious pathogens using computer science and mathematics? Join the Delft Bioinformatics Lab and work on graph-based algorithms for microbial genomics
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that may empower consumers to resist persuasion and reduce harmful consequences? Are you passionate about advertising, algorithms and social media? Then apply for this PhD project focused on demystifying
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, algorithms and social media? Then apply for this PhD project focused on demystifying algorithmic persuasion by commercial advertisers on TikTok from a differential vulnerability perspective. Who is vulnerable
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stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
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. You will be working at the intersection of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping
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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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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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The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing. As machines
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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