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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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The research group Mechanics of Materials and Structures at Ghent University (UGent-MMS) has 4 vacancies for PhD and postdoc research in the field of fibre-reinforced composites. The vacancies
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the preparation of a doctorate, contains: Description: Within the context of the Belgian funded FWO project “Integrated photonic Ising machines” there is currently an open position at the Vrije Universiteit Brussel
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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), machine learning, advanced use of LLMs. Experience with Unix-like environments and software development in the context of large (open-source) software projects is highly valuable. The applicant should be
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increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection
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military performance, presenteeism, and may even lead to costly evacuations during deployments. This PhD project builds upon pioneering research at MHQA and aims to implement and evaluate allergy screening
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put