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on a unique cohort of 1,750 human liver samples and 400 blood samples and integrates high-resolution genomic approaches (ultra-deep WES, ddPCR, single-cell RNA-seq, spatial transcriptomics). Main
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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
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knowledge of deep learning and computer networking/systems is required Experience with AI as a platform and expert use of AI as a tool strongly desired Experience implementing a language model is a plus Refer
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research tasks, mentoring junior researchers, and coordinating multi-stage projects. Fluency in Python and modern deep learning frameworks (PyTorch/TensorFlow). Strong analytical, communication, and academic
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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or continual learning paradigms Research Statement. Two reference letters (one from your PhD supervisor). Please contact Dr. Özlem Durmaz Incel if you have any additional questions via the following
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pillars such as Big Data Analytics and Management, Machine Learning and Optimization, AI for Data Science, Deep Learning, Generative Learning, Biometrics Processing, Natural Language Processing, Computer