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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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to identify those most at risk from extreme heat, as well as offering personalized adaptation advice --- translating rich multi-modal data into interpretable, scalable prediction and advising models. ICARUS
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modeling. The aim of the thesis is to identify and quantify the mechanisms through which the characteristics of the sound environment—in particular the tempo, frequency, tonality, and intensity of music
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experiments. Creating computational and analytical models to match experimental data. Fabricating and testing novel medical sensors technologies. Basic Qualifications Ph.D. in biomedical engineering, medical
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biology, and mechanisms of drug resistance. Research activities will utilize a combination of in vitro approaches, including cultured cell models, and in vivo experimental systems using animal models
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from this PhD project into an agent-based model. This model will be developed by other PhDs in the project team and simulates household adaptation behaviour over time in global flood-prone regions
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. - Conduct high-throughput serum proteomic analyses and integrate molecular datasets. - Validate candidate biomarkers in independent cohorts. WP3.2 – Integrated predictive modeling: - Develop integrative multi
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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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lung fibrosis. The ideal candidate will independently perform studies utilizing established in vitro, ex vivo and in vivo preclinical models and will have the opportunity to develop and refine novel
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on the development of mathematical and computational models of biological systems. This position is supported by an NIH Virtual Consortium for Translational/Transdisciplinary Environmental Research (ViCTER) grant, “In