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large-scale omics datasets, develop and apply statistical methods and interpretable AI models, and contribute to the identification of biological markers and molecular mechanisms associated with disease
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to develop and utilize innovative, interpretable data-driven analysis methods to significantly advance our understanding of immune cell inter-relations within the cancer microenvironment. We will apply
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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models for intelligent environments. The primary area of research involves developing AI models that can learn to represent real-world phenomena based on various types of observations, including video
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role in enabling this transition, while also introducing new challenges related to system dynamics, stability, and control. This project focuses on the development of modeling, stability analysis
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nutrient cycles and producing renewable energy and nutrient-rich biofertilizers. Biogas production also contributes to increased preparedness and self-sufficiency. The biogas process is driven by complex
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to the development of next-generation approaches for marine environmental assessment and sustainability, working at the interface of environmental systems science, marine technology, and data-driven research within