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and Energy (HDS-LEE), the project offers an interdisciplinary research environment at the interface of bioengineering, computational biophysics, and data-driven modeling, with strong links to open
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rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very relevant. The steps in the development of surrogate models are building data-driven models from medical imaging
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approaches across a range of model organisms to understand how and why we age. As a PhD candidate at FLI, you’ll be part of an international and interdisciplinary environment where basic science meets
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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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engineering, biotechnology, computational biophysics, bioinformatics, data science, or a closely related discipline with a strong academic record Genuine interest in data-driven and physics-based modeling
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relevant. The steps in the development of surrogate models are building data-driven models from medical imaging, extending them with physics-based approaches, and adapting existing physics-integrated neural
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number variations, are major risk factors for adverse mental health outcomes. Beyond their clinical importance, they provide powerful human models for uncovering multiscale genetic mechanisms in psychiatry
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framework capable of accurately predicting pollutant transport and dispersion in coastal waters. By combining high-fidelity numerical simulations with data driven surrogate models, the proposed research aims
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experiments, behavioral research, econometric and causal inference approaches, optimization and analytical modeling, and data-driven techniques such as machine learning and large language models. Our work is