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Field
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for resilient, just, and sustainable Southeast Asian societies. CTP will generate knowledge and innovation across disciplines including climate and Earth science, ecology, materials science, artificial
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such artificial empathy, and how this relates to AI sycophancy and mental health. For more information about the research conducted in the lab, please visit the lab website at https://cascoglab.psy.utexas.edu
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Research, Health Economics, Biostatistics, Data Science, or a closely related field (completed by start date). Strong quantitative skills, including experience with statistical modeling and/or data science
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record in machine learning, with preference for expertise in representation learning, deep embeddings, contrastive learning, or foundation models. Solid background in linear algebra, probability, and
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illuminate data, interprets reports, evaluate and criticise texts and bring new insights. Ability to creatively apply relevant research approaches, models, techniques and methods. Ability to assess and
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, trustworthy AI systems that boost productivity, resilience, and environmental performance. The hub-and-spoke model connects universities as research “spokes” to address real-world challenges across priority
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modifications of the biomolecular landscape of bacterial cells contribute to bacterial physiology, pathogenesis and drug susceptibility, iii) understanding and modelling pathogenesis and treatment failure
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models. Working with conventional laboratory reagents, instrumentation, and molecular/tissue biology techniques. Operating and generating data utilizing advanced instrumentation/techniques, flow cytometry
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will be required to provide a mass value and model-based uncertainty using multiple methods to cross-check. These methods may include mass spectrometry and inertial balance development and prototype
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical