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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from
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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting
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. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with implications
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cellular reprogramming strategies and identify new omics-based biomarkers. We work closely with clinical partners and we focus on deep understanding of molecular mechanisms of disease development. PhD
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educates the next generation of structural engineers, equipping them with deep technical knowledge and top-level competencies in the use of timber as a high-quality building material, contributing
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models of protein structures and complexes for application in life sciences. In addition, we develop and maintain PLINDER, a resource designed to drive breakthroughs in deep learning-based protein-ligand