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to describe ocean turbulent fluxes #developing theoretical and conceptual models to understand and predict ocean mixing #work as an integrative part of a motivated multidisciplinary team within the institute
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, traditional risk prediction models like the Steno Type 1 Risk Engine fail to account for the immunological dysregulation inherent in T1D. Project Objective The PhD candidate will primarily focus on the clinical
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mathematical models, validating them with experimental data, and making predictions. The ultimate goal is to decode the mechanisms behind intra-organelle coordination. Besides plant cells, such coordination is a
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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to gain insights into the genetic underpinnings of disease and improve genetic risk prediction. We seek to build on previous expertise and methods devised by our teams (see below), including incorporating
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highly accurate computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW
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to biological data sets such as omics data, protein structure prediction, or biomedical imaging. Technical experience in programming (Python preferred), and/or machine learning is a plus—not a requirement. We