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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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» Cognitive science Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 24 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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insights for best practices and design guidelines. Collaborate across the TACIT project: Collaborate with other PhD candidates and researchers working on the project to share insights and learn from its
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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, materials science, or computer science with an interest in complex materials for computing and learning. Excellent candidates with training in any area of science or engineering will be considered. PhD
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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models