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, or a closely related discipline with a strong academic record Genuine interest in data-driven and physics-based modeling, molecular simulations, and their application to bioprocesses and bioseparations
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Investigate instance and panoptic segmentation for endosymbionts and track them over time Implement, train and test novel machine-learning-based solutions on top-tier super-computing hardware Work in an
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population-level neural interactions. Prior work has emphasized rate-based codes due to their relative simplicity; our approach will explicitly extend these models to capture temporal structure within spike
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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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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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submanifolds and their temporal dynamics during behavior Leverage dimensionality reduction and regression models to isolate task-related submanifolds and their respective role for sensory processing and task
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interpretable, synthesis-proximal modifications to known materials. Create generative models for material discovery adhering to strict physical constraints needed for stable and synthesizable crystal structures