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We are looking for a PhD student in Visual Learning and Communication with a focus on interactive visualization, visual learning, science communication, and educational science, formally based
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multiple 2D images and multiple channels, (d) optimizing 2D projection viewpoints (dose reduction and time savings), (e) applying artificial intelligence and traditional machine learning models to noise
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perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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of this PhD is to develop physics-informed neural operator frameworks that embed governing equations and invariants of fluid mechanics directly into learning architectures, enabling real-time, generalizable
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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Intelligence and machine-learning approaches and emerging digital technologies such as non-contact sensors, smartphones, and computer tablets. This theme could also include research in data analytics and
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of Vienna). About the position: Lead the research group focusing on hybrid quantum algorithms, quantum neuromorphic computing, and quantum machine learning Build your own team (PhD students, postdocs) 4-year