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experimental models that bridge laboratory discoveries to clinical applications. The program emphasises interdisciplinary collaboration across orthopaedics, otology, and odontology, integrating basic research
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(CSA) strategies and technologies—such as climate-resilient crop varieties, precision agriculture, rainwater harvesting, intercropping, crop diversification, integrated pest management, and agroforestry
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goal is to integrate physical priors — including periodicity, symmetry, and long-range correlations — directly into the learning process to achieve more robust, interpretable, and scientifically
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). The intention ius also to explore hybrid models that combine the local sensitivity of CNNs with the global modeling capabilities of these emerging architectures. A central goal is to integrate physical priors
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methods in fluid dynamics and heat transfer to study multiphase flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict
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harvesting, intercropping, crop diversification, integrated pest management, and agroforestry—and their impacts on welfare indicators like food and nutritional security, income, inequality, multidimensional
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subsequently be integrated into treatment planning to mitigate the risk of lymphopenia. The implementation of such models has the potential to minimize radiation-induced lymphopenia and thereby improve post
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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tailored materials design with advanced characterization methods to enable new device functionalities. The research aims to expand the capabilities of organic electronic devices by integrating light
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from manufacturing sensors and XR headsets (video, audio, motion). This includes building multimodal AI pipelines, generating procedural representations, and contributing to the integration of real-time