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Are you interested in developing AI methods that can make physics simulations orders of magnitude faster? If the answer is yes, please continue reading! Join our team! The University of Amsterdam
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learning, artificial intelligence, and its applications to large scale data domains in science and industry. This includes the development of deep generative models, methods for approximate inference
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passionate about linking atomistic processes to device performance through computer simulations? Are you fascinated by designing next-generation semiconductors with quantum-mechanical methods enhanced by AI
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estimation, and tests for interactions; Designing methods to overcome numerical challenges. What do you have to offer You have a PhD in a topic that involved Bayes factor hypothesis testing. You can
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methods, integrating qualitative approaches (e.g., interviews, walk-alongs, focus groups) with quantitative techniques (e.g., computer vision, physiological sensing, environmental monitoring, crowd
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-statisticians challenge and workshop; Studying model-averaging, effect size estimation, and tests for interactions; Designing methods to overcome numerical challenges. What do you have to offer You have a PhD in
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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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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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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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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