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Field
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Groningen as a PhD student. Your tasks Develop a bird inspired algorithm and robot capable of magnetic navigation like a bird using the guidance, navigation and control framework as a robotics basis. Develop
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that are both fast and adaptive? This thesis aims to develop a robust hybrid learning framework that lies at the nexus of online and offline learning. The developed algorithms should be able to benefit from
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explainable AI for large-scale and complex datasets by developing algorithms, pipelines, and tools suitable for critical decision-making contexts. The doctoral student will be based at the Health Technology
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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consistent thermodynamic framework; Algorithm development for the numerical resolution of the resulting systems; Numerical simulations and validation of the proposed models. The model will be formulated in
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thermodynamic framework; Algorithm development for the numerical resolution of the resulting systems; Numerical simulations and validation of the proposed models. The model will be formulated in terms of gradient
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. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ Background and description of tasks Our group develops new single-cell multiomic methods to characterize microbial
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management systems (BMS). Ability to develop and implement algorithms for modelling, estimation, or control applications. Strong analytical thinking, problem-solving ability, and capability to conduct
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale