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- Delft University of Technology (TU Delft); yesterday published
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- Max Planck Institute for Psycholinguistics; today published
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- Radboud University Medical Center (Radboudumc); 10 Oct ’25 published
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Radboud University Medical Center (Radboudumc); 10 Oct ’25 published | Netherlands | about 2 months ago
Image Analysis Group (DIAG) at Radboudumc. We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided
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living lab approach. Your role will involve employing a broad spectrum of data collection and knowledge acquisition methods, integrating qualitative approaches (e.g., interviews, walk-alongs, focus groups
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(LLMs). Developing methods to improve the safety and robustness of LLMs. Enabling effective collaboration between intelligent agents. The research is funded by NWO (Dutch Science Foundation
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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in context, e.g., reacting to a partner’s gesture, resembling an object they refer to in the environment. Exploring how these models can enhance human-computer interaction and improve our understanding
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enhance human-computer interaction and improve our understanding of multimodal communication. To do so, you will have full access to motion-capture and virtual-reality labs, 3D animation tools, and GPU
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psycholinguistics, brain-computer interfaces, robotics, cognitive modeling of language, computational linguistics, educational technologies, computational modeling of evolutionary and adaptive systems, image and
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land management systems. Third, you will develop LCA characterisation factors by integrating the biodiversity response estimates with maps of agricultural management systems in species-area models
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will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models for phenotype prediction, helping to accelerate the cell
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cell lines have been engineered and characterised, you will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models