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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/compensation-tools.php CBC Requirement
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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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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the project RIBOTICS (RNA Origami Technology in Cell Systems) funded by an Advanced Grant from the European Research Council (ERC). The intended starting date is 01st September 2026 or as soon as possible
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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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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due