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, population-level life course perspective on the benefits and risks of hormonal contraceptives. By integrating big data and advanced methods for modeling exposures, we will establish real-world patterns
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of computer graphics, human-computer interaction, computer vision, and machine learning. Conducting comprehensive literature reviews in related areas, including deep generative models, image and video synthesis
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work on adapting or developing marine foundation models. Self-supervised learning and active learning are also possible research topics. You can also focus on challenges related to modelling physics
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in the μ-opioid receptor. The PhD candidate will: • generate high-resolution models of wild-type and R181C mutant μ-opioid receptor based on existing crystallographic data and perform molecular
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involves building and training deep-learning models for TEM image reconstruction and interpretation, linking image features to local chemistry, defects, and dynamic transformations under varying
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learning methods for generating complex structures for generation of virus capsids and therapeutic proteins for gene therapy. The project will involve both physics-based forward modeling of protein
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candidate to develop Machine Learning models and frameworks for time series analysis, aimed at understanding how the human brain encodes information. This cross-disciplinary project is a high-level
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of hypothermia with important medical implications. To study this, the Ph.D. candidate will use mammalian cells, organoids, and animal models, and employ a range of approaches, including cell culture systems
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sections on conceptual models, methodology, and significance. Collaborate with CARE leadership and project teams to prepare submissions to NIH, DoD, and private foundations. Develop an independent line
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-dimensional model of Antarctic glacial isostatic adjustment Full Time, 3.5 year fixed-term position based in Hobart About the opportunity This postdoctoral research position will work on reconstruction