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from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
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classification for conducting cutting-edge and life-changing research that creates impact in our communities. Additionally, for more than a decade, they have received a national Military Friendly® School
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development in topics such as computer vision, audio signal processing, machine learning, deep learning, and/or sensor systems. Experience in collaboration and technology transfer to partners outside
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to the development of novel indoor localization and tracking methods, algorithms, and systems ? Evaluating the performance of such methods, algorithms, and systems via modeling and simulation ? Performance evaluation
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of distance learning educational model required Able to gather, analyze, evaluate, and integrate information electronically In-depth knowledge of distance learning educational models, adult learning styles, and
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-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning frameworks that integrate
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research agenda using advanced quantitative methods—such as machine learning, computational modeling, big-data analytics, and wearable technologies—to study tourism, hospitality, and/or human performance
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, or probabilistic modeling, and be proficient in Python and modern machine-learning frameworks (ideally PyTorch). Experience with single-cell transcriptomics, epigenomics, proteomics, spatial omics, or multimodal
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 11 days ago
establish reference performance and guide the design of more advanced models. The core of the work will then focus on developing and evaluating machine-learning-based NILM methods tailored